This document provides an overview of Hadoop MapReduce. It begins with an introduction to MapReduce and defines it as the processing component of Apache Hadoop that processes data in parallel across a distributed environment. The document then discusses two main advantages of MapReduce: 1) parallel processing, which makes data processing fast, and 2) data locality where processing is moved to the data rather than moving large amounts of data. It also provides an example of how MapReduce can be used to efficiently count words in a document by splitting the work across nodes and aggregating the results.

1. Hadoop
MapReduce

2. HELLO!
I am Yogender Singh
Working with Entrench Electronic and penthao
as big data and spark developer.

3. Map reduce
Introduction

4. Agenda For Today’s Session
◉ What is hadoop MapReduce ?
◉ MapReduce In Nutshell
◉ Two Advantages of MapReduce
◉ Hadoop MapReduce Approach with an Example

5. What is mapreduce?
Components of mapreduce

6. Storage
Hadoop Components
Processing
2 main Hadoop Components

7. MapReduce Data Processing and
Programming
◉ MapReduce is the processing components
of Apache Hadoop
◉ It process data parallelly in distributed
environment
Result

8. MapReduce In Nutshell

9. 2 Biggest Adavantages
Of MapReduce

10. Advantage 1 : Parallel Processing
◉ Data is processed in parallel.
◉ Processing become fast.
Data

11. Advantage 2 : Data Locality – Processing to Storage
◉ Moving Data to processing is very
costly.
◉ In MapReduce , we move processing to
data.
Data

12. Traditional vs Mapreduce

13. Election votes counting : Traditional way
Election Vote Casting
◉ vote is stored at different Booths
◉ Result Center has the details of
all the Booths

14. Election votes counting : MapReduce way
Counting - MapReduce Approach
◉ Votes are counted at individual booths.
◉ Booth-wise result are send back to the result
Centre.
◉ Final Result is declared easily and quickly
using this way.

15. MapReduce way :
INPUT
OUTPUT
Map()
Map()
Map()
Reduce()
Reduce()

16. Anatomy of a MapReduce Program
MapReduce
ValueKey
(k1,v1)
List(k3,v3)(k2,list(v2))
List(K2,v2)
Map :
Reduce :

17. Example (MapReduce Problem)

18. MapReduce way – Word Count Process
The overall MapReduce Word Count Process
Input Split
Deer Bear River
Car Car River
Deer Car Bear
Deer Bear River
Car Car River
Splitting

19. MapReduce way – Word Count Process
The overall MapReduce Word Count Process
Input Split
Deer Bear River
Car Car River
Deer Car Bear
Deer Bear River
Car Car River
Deer Car Bear
Splitting
Deer,1
Bear,1
River,1
Car,1
Car,1
River,1
Mapping

20. MapReduce way – Word Count Process
The overall MapReduce Word Count Process
Input Split
Deer Bear River
Car Car River
Deer Car Bear
Deer Bear River
Car Car River
Deer Car Bear
Splitting
Deer,1
Bear,1
River,1
Car,1
Car,1
River,1
Deer,1
Car,1
Bear,1
Bear,(1,1)
Car,(1,1,1)
Dear,(1,1)
Mapping Shuffling

21. MapReduce way – Word Count Process
The overall MapReduce Word Count Process
Input Split
Deer Bear River
Car Car River
Deer Car Bear
Deer Bear River
Car Car River
Deer Car Bear
Splitting
Deer,1
Bear,1
River,1
Car,1
Car,1
River,1
Deer,1
Car,1
Bear,1
Bear,(1,1)
Car,(1,1,1)
Dear,(1,1)
River,(1,1)
Mapping
Bear,2
Car,3
Dear,2
Shuffling Reducing

22. MapReduce way – Word Count Process
The overall MapReduce Word Count Process
Input Split
Deer Bear River
Car Car River
Deer Car Bear
Deer Bear River
Car Car River
Deer Car Bear
Splitting
Deer,1
Bear,1
River,1
Car,1
Car,1
River,1
Deer,1
Car,1
Bear,1
Bear,(1,1)
Car,(1,1,1)
Dear,(1,1)
River,(1,1)
Mapping
Bear,2
Car,3
Dear,2
River,2
Shuffling Reducing Final Result
Bear,2
Car,3
Dear,2
River,2

23. THANKS!
Any questions?
You can reach me
yash991314@gmail.com / yogi@entrench.org