Your first Hadoop Map-Reduce Job

Introduction

Hadoop Map-Reduce is a YARN-based system for parallel processing of large data sets. If you are new to hadoop, first visit here. In this article, I will help you quickly start with writing the simplest Map-Reduce job. This is a famous “Wordcount” MR job and the first one for 90% of the people (if not more).

WordCount is a simple application that counts the number of occurrences of each word in a given input set.

This code example is from MapReduce tutorial available here. You can checkout source code directly from this small Github project I created.

Step 1. Install and start Hadoop server

In this tutorial, I assume your hadoop installation is ready. For Single Node setup,visit here.

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, SecondaryNameNode are running)

Step 2. Write Map-Reduce 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 POM file here and add to your java project. This will make sure you have Hadoop Jar dependency ready.

Just Run:

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 Map-Reduce 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

 

Hi, I am Amresh. I work for R&D lab of Software Product Engineering firm Impetus Technologies.

I am technology enthusiast and love reading…mostly news and magazine articles. I am a fitness freak and love watching movies and listening to music. My Blog

 

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By | 2017-07-14T22:13:18+00:00 October 9th, 2012|Hadoop|6 Comments

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6 Comments

  1. Chris August 8, 2013 at 2:02 am - Reply

    Thanks for this tutorial, it was of great help. I am reading Hadoop The Definitive Guide but had a difficult time with what they were talking about and wanted to run an example, and you have a great concise example!

    • admin August 8, 2013 at 2:18 am - Reply

      Hi Chris,

      Thanks for your appreciation.

  2. fred July 15, 2015 at 5:54 pm - Reply

    I am having trouble with step three. Should I be adjusting the pom.xml file in order for mvn clean install to work?

  3. Vishal January 5, 2016 at 3:34 pm - Reply

    your pom file should be parallel to your src directory.

  4. aditya May 3, 2016 at 1:12 pm - Reply

    Thanks for this amresh. 🙂
    Being able to run some code is always better than just reading books.
    It was great help.

  5. MJ August 13, 2016 at 6:42 pm - Reply

    Very helpful Thanks

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