Your First Hadoop MapReduce Job

Hadoop MapReduce is a YARN-based system for parallel processing of large data sets. If you are new to Hadoop, first explore the Hadoop site. In this article, I'll help you quickly start writing the simplest MapReduce job. This famous WordCount job is the first that many Hadoop beginners write: a simple application that counts the number of occurrences of each word in a given input set.

You can check out the source code directly from this small GitHub project I created.

Step 1: Install and Start the Hadoop Server

In this tutorial, I will assume your Hadoop installation is ready. 

Start Hadoop:

amresh@ubuntu:/home/amresh$ cd /usr/local/hadoop/
amresh@ubuntu:/usr/local/hadoop-1.0.2$ bin/start-all.sh
amresh@ubuntu:/usr/local/hadoop-1.0.2$ sudo jps

6098 JobTracker
8024 Jps
5783 DataNode
5997 SecondaryNameNode
5571 NameNode
6310 TaskTracker

(Make sure NameNode, DataNode, JobTracker, TaskTracker, and SecondaryNameNode are running.)

Step 2: Write the MapReduce Job for WordCount

Map.java (Mapper Implementation)

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);
 }
 }
}

Reduce.java (Reducer Implementation)

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));
 }
}

WordCount.java (Job)

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);

 }
}

Step 3: Compile and Create Jar File

I prefer Maven for building my Java project. You can find the POM file here and add it to your Java project. This will make sure you have your Hadoop Jar dependency ready.

Just run the following:

amresh@ubuntu:/usr/local/hadoop-1.0.2$ cd ~/development/hadoop-examples
amresh@ubuntu:/home/amresh/development/hadoop-examples$ mvn clean install

Step 4: Create Input Files To Copy Words From

amresh@ubuntu:/usr/local/hadoop-1.0.2$ bin/hadoop dfs -mkdir ~/wordcount/input
amresh@ubuntu:/usr/local/hadoop-1.0.2$ sudo vi file01 (Hello World Bye World)
amresh@ubuntu:/usr/local/hadoop-1.0.2$ sudo vi file02 (Hello Hadoop Goodbye Hadoop)
amresh@ubuntu:/usr/local/hadoop-1.0.2$ bin/hadoop dfs -copyFromLocal file01 /home/amresh/wordcount/input/
amresh@ubuntu:/usr/local/hadoop-1.0.2$ bin/hadoop dfs -copyFromLocal file02 /home/amresh/wordcount/input/
amresh@ubuntu:/usr/local/hadoop-1.0.2$ bin/hadoop dfs -ls /home/amresh/wordcount/input/

Found 2 items
-rw-r--r-- 1 amresh supergroup 0 2012-05-08 14:51 /home/amresh/wordcount/input/file01
-rw-r--r-- 1 amresh supergroup 0 2012-05-08 14:51 /home/amresh/wordcount/input/file02

Step 5: Run the MapReduce Job You Wrote

amresh@ubuntu:/usr/local/hadoop-1.0.2$ bin/hadoop jar ~/development/hadoop-examples/target/hadoop-examples-1.0.jar com.impetus.code.examples.hadoop.mapred.wordcount.WordCount /home/amresh/wordcount/input /home/amresh/wordcount/output
amresh@ubuntu:/usr/local/hadoop-1.0.2$ bin/hadoop dfs -ls /home/amresh/wordcount/output/

Found 3 items
-rw-r--r-- 1 amresh supergroup 0 2012-05-08 15:23 /home/amresh/wordcount/output/_SUCCESS
drwxr-xr-x - amresh supergroup 0 2012-05-08 15:22 /home/amresh/wordcount/output/_logs
-rw-r--r-- 1 amresh supergroup 41 2012-05-08 15:23 /home/amresh/wordcount/output/part-00000

amresh@ubuntu:/usr/local/hadoop-1.0.2$ bin/hadoop dfs -cat /home/amresh/wordcount/output/part-00000

Bye 1
Goodbye 1
Hadoop 2
Hello 2
World 2