Hadoop & Mapreduce Examples: Create your First Program

Problem Statement:

Find out Number of Products Sold in Each Country.

SalesJan2009.csv

Prerequisites:

• This tutorial is developed on Linux - Ubuntu operating System.
  
• You should have Hadoop (version 2.2.0 used for this tutorial) already installed.
  
• You should have Java (version 1.8.0 used for this tutorial) already installed on the system.

Before we start with the actual process, change user to 'hduser' (user used for Hadoop ).

su - hduser_

Transaction date
Product
Price
Payment Type
Name
City
State
Country
Account Created
Last Login
Latitude
Longitude
01-02-2009 6:17
Product1
1200
Mastercard
carolina
Basildon
England
United Kingdom
01-02-2009 6:00
01-02-2009 6:08
51.5
-1.1166667
01-02-2009 4:53
Product1
1200
Visa
Betina
Parkville                   
MO
United States
01-02-2009 4:42
01-02-2009 7:49
39.195
-94.68194
01-02-2009 13:08
Product1
1200
Mastercard
Federica e Andrea
Astoria                     
OR
United States
01-01-2009 16:21
01-03-2009 12:32
46.18806
-123.83
01-03-2009 14:44
Product1
1200
Visa
Gouya
Echuca
Victoria
Australia
9/25/05 21:13
01-03-2009 14:22
-36.1333333
144.75
01-04-2009 12:56
Product2
3600
Visa
Gerd W 
Cahaba Heights              
AL
United States
11/15/08 15:47
01-04-2009 12:45
33.52056
-86.8025
01-04-2009 13:19
Product1
1200
Visa
LAURENCE
Mickleton                   
NJ
United States
9/24/08 15:19
01-04-2009 13:04
39.79
-75.23806

Steps: 1

Create a new directory with name MapReduceTutorial

sudo mkdir MapReduceTutorial

Give permissions

sudo chmod -R 777 MapReduceTutorial

SalesMapper.java 

package SalesCountry;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.*;

public class SalesMapper extends MapReduceBase implements Mapper <LongWritable, Text, Text, IntWritable> {
	private final static IntWritable one = new IntWritable(1);

	public void map(LongWritable key, Text value, OutputCollector <Text, IntWritable> output, Reporter reporter) throws IOException {

		String valueString = value.toString();
		String[] SingleCountryData = valueString.split(",");
		output.collect(new Text(SingleCountryData[7]), one);
	}
}

SalesCountryReducer.java 

package SalesCountry;

import java.io.IOException;
import java.util.*;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.*;

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

	public void reduce(Text t_key, Iterator<IntWritable> values, OutputCollector<Text,IntWritable> output, Reporter reporter) throws IOException {
		Text key = t_key;
		int frequencyForCountry = 0;
		while (values.hasNext()) {
			// replace type of value with the actual type of our value
			IntWritable value = (IntWritable) values.next();
			frequencyForCountry += value.get();
			
		}
		output.collect(key, new IntWritable(frequencyForCountry));
	}
}

SalesCountryDriver.java 

package SalesCountry;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.*;

public class SalesCountryDriver {
	public static void main(String[] args) {
		JobClient my_client = new JobClient();
		// Create a configuration object for the job
		JobConf job_conf = new JobConf(SalesCountryDriver.class);

		// Set a name of the Job
		job_conf.setJobName("SalePerCountry");

		// Specify data type of output key and value
		job_conf.setOutputKeyClass(Text.class);
		job_conf.setOutputValueClass(IntWritable.class);

		// Specify names of Mapper and Reducer Class
		job_conf.setMapperClass(SalesCountry.SalesMapper.class);
		job_conf.setReducerClass(SalesCountry.SalesCountryReducer.class);

		// Specify formats of the data type of Input and output
		job_conf.setInputFormat(TextInputFormat.class);
		job_conf.setOutputFormat(TextOutputFormat.class);

		// Set input and output directories using command line arguments, 
		//arg[0] = name of input directory on HDFS, and arg[1] =  name of output directory to be created to store the output file.
		
		FileInputFormat.setInputPaths(job_conf, new Path(args[0]));
		FileOutputFormat.setOutputPath(job_conf, new Path(args[1]));

		my_client.setConf(job_conf);
		try {
			// Run the job 
			JobClient.runJob(job_conf);
		} catch (Exception e) {
			e.printStackTrace();
		}
	}
}

Download Files Here

If you want to understand the code in these files refer this Guide

Check the file permissions of all these files

and if 'read' permissions are missing then grant the same-

Steps: 2

Export classpath

export CLASSPATH="$HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.2.0.jar:$HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-client-common-2.2.0.jar:$HADOOP_HOME/share/hadoop/common/hadoop-common-2.2.0.jar:~/MapReduceTutorial/SalesCountry/*:$HADOOP_HOME/lib/*"

Steps: 3

Compile Java files (these files are present in directory Final-MapReduceHandsOn). Its class files will be put in the package directory

javac -d . SalesMapper.java SalesCountryReducer.java SalesCountryDriver.java

This warning can be safely ignored.

This compilation will create a directory in a current directory named with package name specified in the java source file (i.e. SalesCountry in our case) and put all compiled class files in it.

Steps: 4

Create a new file Manifest.txt

sudo gedit Manifest.txt

add following lines to it,

Main-Class: SalesCountry.SalesCountryDriver

SalesCountry.SalesCountryDriver is name of main class. Please note that you have to hit enter key at end of this line.

Steps: 5

Create a Jar file

jar cfm ProductSalePerCountry.jar Manifest.txt SalesCountry/*.class

Check that the jar file is created

Steps: 6

Start Hadoop

$HADOOP_HOME/sbin/start-dfs.sh

$HADOOP_HOME/sbin/start-yarn.sh

Steps: 7

Copy the File SalesJan2009.csv into ~/inputMapReduce

Now Use below command to copy ~/inputMapReduce to HDFS.

$HADOOP_HOME/bin/hdfs dfs -copyFromLocal ~/inputMapReduce /

We can safely ignore this warning.

Verify whether file is actually copied or not.

$HADOOP_HOME/bin/hdfs dfs -ls /inputMapReduce

Steps: 8

Run MapReduce job

$HADOOP_HOME/bin/hadoop jar ProductSalePerCountry.jar /inputMapReduce /mapreduce_output_sales

This will create an output directory named mapreduce_output_sales on HDFS. Contents of this directory will be a file containing product sales per country.

Steps: 9

Result can be seen through command interface as,

$HADOOP_HOME/bin/hdfs dfs -cat /mapreduce_output_sales/part-00000

o/p of above

            OR

Results can also be seen via web interface as-

Results through web interface-

Open r in web browser.

Now select 'Browse the filesystem' and navigate upto /mapreduce_output_sales

o/p of above

Open part-r-00000

 

Understanding MapReducer Code

Explanation of SalesMapper Class

In this section we will understand implementation of SalesMapper class.

1. We begin by specifying name of package for our class. SalesCountry is name of out package. Please note that output of compilation, SalesMapper.class will go into directory named by this package name: SalesCountry.

Followed by this, we import library packages.

Below snapshot shows implementation of SalesMapper class-

Code Explanation:

1. SalesMapper Class Definition-

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

Every mapper class must be extended from MapReduceBase class and it must implement Mapper interface.

2. Defining 'map' function-

public void map(LongWritable key,
         Text value,
OutputCollector<Text, IntWritable> output,
Reporter reporter) throws IOException

Main part of Mapper class is a 'map()' method which accepts four arguments.

At every call to 'map()' method, a key-value pair ('key' and 'value' in this code) is passed.

'map()' method begins by splitting input text which is received as an argument. It uses tokenizer to split these lines into words.        

String valueString = value.toString();
String[] SingleCountryData = valueString.split(",");

Here, ',' is used as a delimiter.

After this, a pair is formed using a record at 7th index of array 'SingleCountryData' and a value '1'.

        output.collect(new Text(SingleCountryData[7]), one);

We are choosing record at 7th index because we need Country data and it is located at 7th index in array 'SingleCountryData'.

Please note that our input data is in the below format (where Country is at 7th index, with 0 as a starting index)-

Transaction_date,Product,Price,Payment_Type,Name,City,State,Country,Account_Created,Last_Login,Latitude,Longitude

Output of mapper is again a key-value pair which is outputted using 'collect()' method of 'OutputCollector'.

Explanation of SalesCountryReducer Class

In this section we will understand implementation of SalesCountryReducer class.

1. We begin by specifying name of package for our class. SalesCountry is name of out package. Please note that output of compilation, SalesCountryReducer.class will go into directory named by this package name: SalesCountry.

Followed by this, we import library packages.

Below snapshot shows implementation of SalesCountryReducer class-

Code Explanation:

1. SalesCountryReducer Class Definition-

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

Here, first two data types, 'Text' and 'IntWritable' are data type of input key-value to the reducer.

Output of mapper is in the form of <CountryName1, 1>, <CountryName2, 1>. This output of mapper becomes input to the reducer. So, to align with its data type, Text and IntWritable are used as data type here.

The last two data types, 'Text' and 'IntWritable' are data type of output generated by reducer in the form of key-value pair.

Every reducer class must be extended from MapReduceBase class and it must implement Reducer interface.

2. Defining 'reduce' function-

public void reduce( Text t_key,
             Iterator<IntWritable> values,                           
             OutputCollector<Text,IntWritable> output,
             Reporter reporter) throws IOException {

Input to the reduce() method is a key with list of multiple values.

For example, in our case it will be-

<United Arab Emirates, 1>, <United Arab Emirates, 1>, <United Arab Emirates, 1>,<United Arab Emirates, 1>, <United Arab Emirates, 1>, <United Arab Emirates, 1>.

This is given to reducer as <United Arab Emirates, {1,1,1,1,1,1}>

So, to accept arguments of this form, first two data types are used, viz., Text and Iterator<IntWritable>. Text is a data type of key and Iterator<IntWritable> is a data type for list of values for that key.

The next argument is of type OutputCollector<Text,IntWritable> which collects output of reducer phase.

reduce() method begins by copying key value and initializing frequency count to 0.

        Text key = t_key;
        int frequencyForCountry = 0;

Then, using 'while' loop, we iterate through the list of values associated with the key and calculate the final frequency by summing up all the values.

 

 while (values.hasNext()) {
            // replace type of value with the actual type of our value
            IntWritable value = (IntWritable) values.next();
            frequencyForCountry += value.get();
            
        }

Now, we push the result to the output collector in the form of key and obtained frequency count.

Below code does this-

        output.collect(key, new IntWritable(frequencyForCountry));

Explanation of SalesCountryDriver Class

In this section we will understand implementation of SalesCountryDriver class

1. We begin by specifying name of package for our class. SalesCountry is name of out package. Please note that output of compilation, SalesCountryDriver.class will go into directory named by this package name: SalesCountry.

Here is a line specifying package name followed by code to import library packages.

2. Define a driver class which will create a new client job, configuration object and advertise Mapper and Reducer classes.

The driver class is responsible for setting our MapReduce job to run in Hadoop. In this class, we specify job name, data type of input/output and names of mapper and reducer classes.

3. In below code snippet, we set input and output directories which are used to consume input dataset and produce output, respectively.

arg[0] and arg[1] are the command-line arguments passed with a command given in MapReduce hands-on, i.e.,

$HADOOP_HOME/bin/hadoop jar ProductSalePerCountry.jar /inputMapReduce /mapreduce_output_sales

4. Trigger our job

Below code start execution of MapReduce job-

 try {
            // Run the job 
            JobClient.runJob(job_conf);
        } catch (Exception e) {
            e.printStackTrace();
        }