Khaled Tannir - Optimizing Hadoop for MapReduce
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- Book:Optimizing Hadoop for MapReduce
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Learn how to configure your Hadoop cluster to run optimal MapReduce jobs
Overview
- Optimize your MapReduce job performance
- Identify your Hadoop clusters weaknesses
- Tune your MapReduce configuration
In Detail
MapReduce is the distribution system that the Hadoop MapReduce engine uses to distribute work around a cluster by working parallel on smaller data sets. It is useful in a wide range of applications, including distributed pattern-based searching, distributed sorting, web link-graph reversal, term-vector per host, web access log stats, inverted index construction, document clustering, machine learning, and statistical machine translation.
This book introduces you to advanced MapReduce concepts and teaches you everything from identifying the factors that affect MapReduce job performance to tuning the MapReduce configuration. Based on real-world experience, this book will help you to fully utilize your clusters node resources to run MapReduce jobs optimally.
This book details the Hadoop MapReduce job performance optimization process. Through a number of clear and practical steps, it will help you to fully utilize your clusters node resources.
Starting with how MapReduce works and the factors that affect MapReduce performance, you will be given an overview of Hadoop metrics and several performance monitoring tools. Further on, you will explore performance counters that help you identify resource bottlenecks, check cluster health, and size your Hadoop cluster. You will also learn about optimizing map and reduce tasks by using Combiners and compression.
The book ends with best practices and recommendations on how to use your Hadoop cluster optimally.
What you will learn from this book
- Learn about the factors that affect MapReduce performance
- Utilize the Hadoop MapReduce performance counters to identify resource bottlenecks
- Size your Hadoop clusters nodes
- Set the number of mappers and reducers correctly
- Optimize mapper and reducer task throughput and code size using compression and Combiners
- Understand the various tuning properties and best practices to optimize clusters
Approach
This book is an example-based tutorial that deals with optimizing MapReduce job performance.
Who this book is written for
If you are a Hadoop administrator, developer, MapReduce user, or beginner, this book is the best choice available if you wish to optimize your clusters and applications. Having prior knowledge of creating MapReduce applications is not necessary, but will help you better understand the concepts and snippets of MapReduce class template code.
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Copyright 2014 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either expressed or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: February 2014
Production Reference: 1140214
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78328-565-5
www.packtpub.com
Cover Image by Khaled Tannir (<>)
Author
Khaled Tannir
Reviewers
Wodzimierz Bzyl
Craig Henderson
Mark Kerzner
Acquisition Editor
Joanne Fitzpatrick
Commissioning Editor
Manasi Pandire
Technical Editors
Mario D'Souza
Rosmy George
Pramod Kumavat
Arwa Manasawala
Adrian Raposo
Copy Editors
Kirti Pai
Laxmi Subramanian
Project Coordinator
Aboli Ambardekar
Proofreaders
Simran Bhogal
Ameesha Green
Indexer
Rekha Nair
Graphics
Yuvraj Mannari
Production Coordinators
Manu Joseph
Alwin Roy
Cover Work
Alwin Roy
Khaled Tannir has been working with computers since 1980. He began programming with the legendary Sinclair Zx81 and later with Commodore home computer products (Vic 20, Commodore 64, Commodore 128D, and Amiga 500).
He has a Bachelor's degree in Electronics, a Master's degree in System Information Architectures, in which he graduated with a professional thesis, and completed his education with a Master of Research degree.
He is a Microsoft Certified Solution Developer (MCSD) and has more than 20 years of technical experience leading the development and implementation of software solutions and giving technical presentations. He now works as an independent IT consultant and has worked as an infrastructure engineer, senior developer, and enterprise/solution architect for many companies in France and Canada.
With significant experience in Microsoft .Net, Microsoft Server Systems, and Oracle Java technologies, he has extensive skills in online/offline applications design, system conversions, and multilingual applications in both domains: Internet and Desktops.
He is always researching new technologies, learning about them, and looking for new adventures in France, North America, and the Middle-east. He owns an IT and electronics laboratory with many servers, monitors, open electronic boards such as Arduino, Netduino, RaspBerry Pi, and .Net Gadgeteer, and some smartphone devices based on Windows Phone, Android, and iOS operating systems.
In 2012, he contributed to the EGC 2012 (International Complex Data Mining forum at Bordeaux University, France) and presented, in a workshop session, his work on "how to optimize data distribution in a cloud computing environment". This work aims to define an approach to optimize the use of data mining algorithms such as k-means and Apriori in a cloud computing environment.
He is the author of RavenDB 2.x Beginner's Guide , Packt Publishing .
He aims to get a PhD in Cloud Computing and Big Data and wants to learn more and more about these technologies.
He enjoys taking landscape and night time photos, travelling, playing video games, creating funny electronic gadgets with Arduino/.Net Gadgeteer, and of course, spending time with his wife and family.
You can reach him at <>.
All praise is due to Allah, the Lord of the Worlds. First, I must thank Allah for giving me the ability to think and write.
Next, I would like to thank my wife, Laila, for her big support, encouragement, and patience throughout this project. Also, I would like to thank my family in Canada and Lebanon for their support during the writing of this book.
I would like to thank everyone at Packt Publishing for their help and guidance, and for giving me the opportunity to share my experience and knowledge in technology with others in the Hadoop and MapReduce community.
Thank you as well to the technical reviewers, who provided great feedback to ensure that every tiny technical detail was accurate and rich in content.
Wodzimierz Bzyl works at the University of Gdask, Poland. His current interests include web-related technologies and NoSQL databases. He has a passion for new technologies and introduces his students to them. He enjoys contributing to open source software and spending time trekking in the Tatra mountains.
Craig Henderson graduated in 1995 with a degree in Computing for Real-time Systems and has spent his career working on large-scale data processing and distributed systems. He is the author of an open source C++ MapReduce library for single server application scalability, which is available at https://github.com/cdmh/mapreduce, and he currently researches image and video processing techniques for person identification.
Mark Kerzner holds degrees in Law, Mathematics, and Computer Science. He has been designing software for many years and Hadoop-based systems since 2008. He is the President of SHMsoft, a provider of Hadoop applications for various verticals, a co-founder of the Hadoop Illuminated training and consulting, and also the co-author of the open source book, Hadoop Illuminated . He has also authored and co-authored other books and patents.
I would like to acknowledge the help of my colleagues, in particular Sujee Maniyam, and last but not least, my multitalented family.
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