Hadoop & Mapreduce Examples

Using Hadoop Mapreduce, First of all, start the Hadoop Cluster using the commands given below.

$HADOOP_HOME/sbin/start-dfs.sh

$HADOOP_HOME/sbin/start-yarn.sh

Check by typing jps in the terminal if all the Nodes are running.

Do you remember in the last article we looked at how a word counter works?

Using Hadoop Mapreduce Let’s implement the above.

You need to create three files.

• Reduce.java
• Map.java
• WordCount.java

Reduce.java

package com.impetus.code.examples.hadoop.mapred.wordcount;

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

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;

public class Reduce extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable>
{
public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output,
Reporter reporter) throws IOException
{
int sum = 0;
while (values.hasNext())
{
sum += values.next().get();
}
output.collect(key, new IntWritable(sum));
}
}

Map.java

package com.impetus.code.examples.hadoop.mapred.wordcount;

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
public class Map extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable>
{
private final static IntWritable one = new IntWritable(1);

private Text word = new Text();

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())
{
word.set(tokenizer.nextToken());
output.collect(word, one);
}
}
}

WordCount.java

package com.impetus.code.examples.hadoop.mapred.wordcount;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;

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

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

conf.setMapperClass(Map.class);
conf.setCombinerClass(Reduce.class);
conf.setReducerClass(Reduce.class);

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

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

JobClient.runJob(conf);

}
}

Now you need to compile java files.

There are two ways to compile java files.

mvn clean install

Or run the following command.

javac -d . Map.java Reduce.java WordCount.java

If you used javac -d command then run the following command too.

jar cfm wordcounter.jar Manifest.txt com/impetus/code/examples/hadoop/mapred/wordcount/*.class

Now let’s create an input folder in HDFS.

Hdfs dfs -mkdir ~/wordcount/input

Now we are going to create two input files. 

sudo vi input_one

And put the following content inside it.

And another file.

sudo vi input_two

Using the command below move the file to HDFS file system

hdfs dfs -copyFromLocal input_one ~/wordcount/input/

Do the above for both input files.

Now check if both files have been moved.

hdfs dfs -ls ~/wordcount/input/

Using Hadoop Mapreduce Now run the map-reduce using the command given below.

$HADOOP_HOME/bin/hadoop jar wordcounter.jar /input /output

By running the below-given command you will be able to see the output.

bin/hadoop dfs -cat ~/wordcount/output/part-00000