Hadoop Programming

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Very simple and Well explained hadoop mapreduce programs are given below. 

If you wanna become master in Hadoop programming come to ORIEN IT Hadoop Training

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How to find the word count in a file using hadoop mapreduce functionality and partition the data based on words starting character in best way?

package com.orienit.hadoop.training.wordcount;

import org.apache.hadoop.conf.Configuration;
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.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 WordCountJob implements Tool {

    private Configuration conf;

    @Override
    public Configuration getConf() {
        return conf;    // getting the configuration
    }

    @Override
    public void setConf(Configuration conf) {
        this.conf = conf;    // setting the configuration
    }

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

     // initializing the job configuration

     Job wordCountJob = new Job(getConf());   
        

     // setting the job name

     wordCountJob.setJobName("Orien IT WordCount Job");   
        

      // to call this as a jar

      wordCountJob.setJarByClass(this.getClass());  
     
      // setting custom mapper class        

      wordCountJob.setMapperClass(WordCountMapper.class);    

 

       // setting custom reducer class

      wordCountJob.setReducerClass(WordCountReducer.class);           

       // setting custom combiner class

       wordCountJob.setCombinerClass(WordCountCombiner.class);  

 
       // setting no of reducers
       wordCountJob.setNumReduceTasks(26);      

        // setting custom partitioner class
        wordCountJob.setPartitionerClass(WordCountPartitioner.class);
       
        // setting mapper output key class: K2       
        wordCountJob.setMapOutputKeyClass(Text.class);   
        
        // setting mapper output value class: V2
        wordCountJob.setMapOutputValueClass(LongWritable.class);    

        // setting reducer output key class: K3
        wordCountJob.setOutputKeyClass(Text.class);    
        
        // setting reducer output value class: V3
        wordCountJob.setOutputValueClass(LongWritable.class);          

        // setting the input format class ,i.e for K1, V1 
        wordCountJob.setInputFormatClass(TextInputFormat.class);     

         // setting the output format class 
         wordCountJob.setOutputFormatClass(TextOutputFormat.class);        

         // setting the input file path
         FileInputFormat.addInputPath(wordCountJob, new Path(args[0]));

        // setting the output folder path
         FileOutputFormat.setOutputPath(wordCountJob, new Path(args[1]));
       
         Path outputpath = new Path(args[1]);
         // delete the output folder if exists
         outputpath.getFileSystem(conf).delete(outputpath,true);
       
         // to execute the job and return the status
         return wordCountJob.waitForCompletion(true) ? 0 : -1;  
       
    }

    public static void main(String[] args) throws Exception {
        // start the job providing arguments and configurations
         ToolRunner.run(new Configuration(), new WordCountJob(), args);  
    }

}

package com.orienit.hadoop.training.wordcount;

import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Counter;
import org.apache.hadoop.mapreduce.Mapper;

public class WordCountMapper extends 
    Mapper<LongWritable, Text, Text, LongWritable> {

    private Text text = new Text();
    private final static LongWritable one = new LongWritable(1);

    enum MyCounter {
        MAPS
    };

    @Override
    protected void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {
        String line = value.toString();
        StringTokenizer words = new StringTokenizer(line, " ");
        Counter staticCounter = context.getCounter(MyCounter.MAPS);
        Counter dynamicCounter = context.getCounter("OrienIT", "how many maps");
        while (words.hasMoreTokens()) {
            staticCounter.increment(1); // increment the static counter
            dynamicCounter.increment(1); // increment the dynamic counter
            text.set(words.nextToken());
            context.write(text, one); // write the map output
        }
    };
}

package com.orienit.hadoop.training.wordcount;

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

public class WordCountPartitioner extends Partitioner<Text, LongWritable> {

    @Override
    public int getPartition(Text text, LongWritable lw, int noOfReducers) {
        String word = text.toString().toLowerCase();
        return (Math.abs(word.charAt(0) - 'a')) % noOfReducers;        // return the partition number to write the reducer output
    }
}

package com.orienit.hadoop.training.wordcount;

import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class WordCountReducer extends Reducer<Text, LongWritable, Text, LongWritable> {

    @Override
    protected void reduce(Text key, Iterable<LongWritable> value, Context context) throws IOException,
            InterruptedException {
        long sum = 0;
        while (value.iterator().hasNext()) {
            sum += value.iterator().next().get();
        }
        context.write(key, new LongWritable(sum));    // write the reducer output
    };
}

package com.orienit.hadoop.training.wordcount;

import java.io.IOException;
import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

public class WordCountCombiner extends Reducer<Text, LongWritable, Text, LongWritable> {

    protected void reduce(Text key, Iterable<LongWritable> value, Context context) throws IOException,

            InterruptedException {

        long sum = 0;

        while (value.iterator().hasNext()) {

            sum += value.iterator().next().get();

        }

        context.write(key, new LongWritable(sum));   

    };

}

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Write a Distributed Grep programming

package com.orienit.hadoop.training.grep;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
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 GrepJob implements Tool {

    private Configuration conf;

    @Override
    public Configuration getConf() {
        return conf;
    }

    @Override
    public void setConf(Configuration conf) {
        this.conf = conf;
    }

    @Override
    public int run(String[] args) throws Exception {
        Job grepJob = new Job(getConf());
        grepJob.setJobName("OrienIT Grep Job");
        grepJob.setJarByClass(this.getClass());
        grepJob.setMapperClass(GrepMapper.class);
        grepJob.setNumReduceTasks(0);
        grepJob.setOutputKeyClass(Text.class);
        grepJob.setOutputValueClass(NullWritable.class);

        grepJob.setInputFormatClass(TextInputFormat.class);
        grepJob.setOutputFormatClass(TextOutputFormat.class);

        FileInputFormat.setInputPaths(grepJob, new Path(args[0]));
        FileOutputFormat.setOutputPath(grepJob, new Path(args[1]));
      
        Path outputpath = new Path(args[1]);
        outputpath.getFileSystem(conf).delete(outputpath,true);
      
        return grepJob.waitForCompletion(true) == true ? 0 : -1;
    }

    public static void main(String[] args) throws Exception {
        Configuration conf1 = new Configuration();
        conf1.set("grep-arg", "Hyderabad");
        ToolRunner.run(conf1, new GrepJob(), args);
    }

}


package com.orienit.hadoop.training.grep;

import java.io.IOException;

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

public class GrepMapper extends Mapper<LongWritable, Text, Text, NullWritable> {

    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        if (value.toString().contains(context.getConfiguration().get("grep-arg"))) {
            context.write( value, NullWritable.get());
        }
    };

}
  

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Write a Distributed Sed programming

package com.orienit.hadoop.training.sed;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
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 SedJob implements Tool {

    private Configuration conf;

    @Override
    public Configuration getConf() {
        return conf;
    }

    @Override
    public void setConf(Configuration conf) {
        this.conf = conf;
    }

    @Override
    public int run(String[] args) throws Exception {
        Job sedjob = new Job(getConf());
        sedjob.setJobName("OrienIT sed Count");
        sedjob.setJarByClass(this.getClass());
        sedjob.setMapperClass(SedMapper.class);
        sedjob.setNumReduceTasks(0);
        sedjob.setOutputKeyClass(Text.class);
        sedjob.setOutputValueClass(NullWritable.class);

        sedjob.setInputFormatClass(TextInputFormat.class);
        sedjob.setOutputFormatClass(TextOutputFormat.class);

        FileInputFormat.setInputPaths(sedjob, new Path(args[0]));
        FileOutputFormat.setOutputPath(sedjob, new Path(args[1]));
       
        Path outputpath = new Path(args[1]);
        outputpath.getFileSystem(conf).delete(outputpath,true);
       
        return sedjob.waitForCompletion(true) == true ? 0 : -1;
    }

    public static void main(String[] args) throws Exception {
        Configuration conf1 = new Configuration();

        conf1.set("sed-arg1", "hadoop");
        conf1.set("sed-arg2", "BigData");
        ToolRunner.run(conf1, new SedJob(), args);
    }

}


package com.orienit.hadoop.training.sed;

import java.io.IOException;

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

public class SedMapper extends Mapper<LongWritable, Text, Text, NullWritable> {

    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        if (value.toString().contains(context.getConfiguration().get("sed-arg1"))) {
            context.write( new Text(value.toString().replaceAll(
                    context.getConfiguration().get("sed-arg1"), context.getConfiguration().get("sed-arg2"))),NullWritable.get());
        } else {
            context.write(value,NullWritable.get());
        }

    }
}

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