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WordCount Example





Example 1: Word count

Input:  orange mango banana
                orange mango banana
                orange mango banana
  orange mango banana

Program:
package mypackage;
import java.io.IOException;
import java.util.Iterator;
import java.util.StringTokenizer;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;

public class wordcount_hadoop {

                public static class Map extends MapReduceBase implements
                                                Mapper<LongWritable, Text, Text, IntWritable> {

                                @Override
                                public void map(LongWritable key, Text value,
                                                                OutputCollector<Text, IntWritable> output, Reporter reporter)
                                                                throws IOException {
                                                String line = value.toString();
                                                StringTokenizer tokenizer = new StringTokenizer(line);
                                                while (tokenizer.hasMoreTokens()) {
                                                                value.set(tokenizer.nextToken());
                                                                output.collect(value, new IntWritable(1));
                                                }
                                }
                }

                public static class Reduce extends MapReduceBase implements
                                                Reducer<Text, IntWritable, Text, IntWritable> {

                                @Override
                                public void reduce(Text key, Iterator<IntWritable> values,
                                                                OutputCollector<Text, IntWritable> output, Reporter reporter)
                                                                throws IOException {
                                                int sum = 0;
                                                while (values.hasNext()) {
                                                                sum = sum + values.next().get();

                                                }
                                                output.collect(key, new IntWritable(sum));
                                }
                }

                public static void main(String[] args) throws Exception {
                                JobConf newconf = new JobConf(wordcount_hadoop.class);
                                newconf.setJobName("wordcount_hadoop");

                                newconf.setOutputKeyClass(Text.class);
                                newconf.setOutputValueClass(IntWritable.class);

                                newconf.setMapperClass(Map.class);
                                newconf.setReducerClass(Reduce.class);
                                //newconf.setCombinerClass(Reduce.class);

                                newconf.setInputFormat(TextInputFormat.class);
                                newconf.setOutputFormat(TextOutputFormat.class);

                                FileInputFormat.setInputPaths(newconf, new Path(args[0]));
                                FileOutputFormat.setOutputPath(newconf, new Path(args[1]));

                                JobClient.runJob(newconf);
                }

}

Output:
Orange – 4
Mango – 4
Banana – 4



How to run in cluster:

*       Start the cluster and make sure datanode,namenode,secondarynamenode,jobtracker and tasktracker running.

*       Move the input file from local file system to HDFS
       Hadoop dfs –copyFromLocal <local_path> <HDFS_path>

*       Run map/reduce program
        Hadoop jar <jar input path> <hadoop output path>
- See more at: http://labstrikes.blogspot.in/2012/08/adsense-middle-blog-post.html#sthash.gQgSkqx8.dpuf
 
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