Spark for Python Developers

206 Pages·2015·4.42 MB·English

by  Amit Nandi

Spark for Python Developers
A concise guide to implementing Spark big data
analytics for Python developers and building a real-time
and insightful trend tracker data-intensive app
Amit Nandi
BIRMINGHAM - MUMBAI
Spark for Python Developers
Copyright © 2015 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 express 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: December 2015
Production reference: 1171215
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78439-969-6
www.packtpub.com
Credits
Author Project Coordinator
Amit Nandi Suzanne Coutinho
Reviewers Proofreader
Manuel Ignacio Franco Galeano Safis Editing
Rahul Kavale
Daniel Lemire Indexer
Priya Sane
Chet Mancini
Laurence Welch
Graphics
Kirk D'Penha
Commissioning Editor
Amarabha Banerjee
Production Coordinator
Shantanu N. Zagade
Acquisition Editor
Sonali Vernekar
Cover Work
Shantanu N. Zagade
Content Development Editor
Merint Thomas Mathew
Technical Editor
Naveenkumar Jain
Copy Editor
Roshni Banerjee
About the Author
Amit Nandi studied physics at the Free University of Brussels in Belgium,
where he did his research on computer generated holograms. Computer generated
holograms are the key components of an optical computer, which is powered by
photons running at the speed of light. He then worked with the university Cray
supercomputer, sending batch jobs of programs written in Fortran. This gave him
a taste for computing, which kept growing. He has worked extensively on large
business reengineering initiatives, using SAP as the main enabler. He focused for the
last 15 years on start-ups in the data space, pioneering new areas of the information
technology landscape. He is currently focusing on large-scale data-intensive
applications as an enterprise architect, data engineer, and software developer.
He understands and speaks seven human languages. Although Python is his
computer language of choice, he aims to be able to write fluently in seven
computer languages too.
Acknowledgment
I want to express my profound gratitude to my parents for their unconditional love
and strong support in all my endeavors.
This book arose from an initial discussion with Richard Gall, an acquisition
editor at Packt Publishing. Without this initial discussion, this book would never
have happened. So, I am grateful to him. The follow ups on discussions and the
contractual terms were agreed with Rebecca Youe. I would like to thank her for her
support. I would also like to thank Merint Mathew, a content editor who helped me
bring this book to the finish line. I am thankful to Merint for his subtle persistence
and tactful support during the write ups and revisions of this book.
We are standing on the shoulders of giants. I want to acknowledge some of the
giants who helped me shape my thinking. I want to recognize the beauty, elegance,
and power of Python as envisioned by Guido van Rossum. My respectful gratitude
goes to Matei Zaharia and the team at Berkeley AMP Lab and Databricks for
developing a new approach to computing with Spark and Mesos. Travis Oliphant,
Peter Wang, and the team at Continuum.io are doing a tremendous job of keeping
Python relevant in a fast-changing computing landscape. Thank you to you all.
About the Reviewers
Manuel Ignacio Franco Galeano is a software developer from Colombia. He
holds a computer science degree from the University of Quindío. At the moment of
publication of this book, he was studying to get his MSc in computer science from
University College Dublin, Ireland. He has a wide range of interests that include
distributed systems, machine learning, micro services, and so on. He is looking for
a way to apply machine learning techniques to audio data in order to help people
learn more about music.
Rahul Kavale works as a software developer at TinyOwl Ltd. He is interested in
multiple technologies ranging from building web applications to solving big data
problems. He has worked in multiple languages, including Scala, Ruby, and Java,
and has worked on Apache Spark, Apache Storm, Apache Kafka, Hadoop, and Hive.
He enjoys writing Scala. Functional programming and distributed computing are his
areas of interest. He has been using Spark since its early stage for varying use cases.
He has also helped with the review for the Pragmatic Scala book.
Daniel Lemire has a BSc and MSc in mathematics from the University of Toronto
and a PhD in engineering mathematics from the Ecole Polytechnique and the
Université de Montréal. He is a professor of computer science at the Université du
Québec. He has also been a research officer at the National Research Council of
Canada and an entrepreneur. He has written over 45 peer-reviewed publications,
including more than 25 journal articles. He has held competitive research grants for
the last 15 years. He has been an expert on several committees with funding agencies
(NSERC and FQRNT). He has served as a program committee member on leading
computer science conferences (for example, ACM CIKM, ACM WSDM, ACM SIGIR,
and ACM RecSys). His open source software has been used by major corporations
such as Google and Facebook. His research interests include databases, information
retrieval and high-performance programming. He blogs regularly on computer
science at http://lemire.me/blog/.
Chet Mancini is a data engineer at Intent Media, Inc in New York, where he
works with the data science team to store and process terabytes of web travel data
to build predictive models of shopper behavior. He enjoys functional programming,
immutable data structures, and machine learning. He writes and speaks on topics
surrounding data engineering and information architecture.
He is a contributor to Apache Spark and other libraries in the Spark ecosystem.
Chet has a master's degree in computer science from Cornell University.
www.PacktPub.com
Support files, eBooks, discount offers, and more
For support files and downloads related to your book, please visit www.PacktPub.com.
Did you know that Packt offers eBook versions of every book published, with PDF
and ePub files available? You can upgrade to the eBook version at www.PacktPub.com
and as a print book customer, you are entitled to a discount on the eBook copy. Get in
touch with us at [email protected] for more details.
At www.PacktPub.com, you can also read a collection of free technical articles, sign
up for a range of free newsletters and receive exclusive discounts and offers on Packt
books and eBooks.
TM
https://www2.packtpub.com/books/subscription/packtlib
Do you need instant solutions to your IT questions? PacktLib is Packt's online digital
book library. Here, you can search, access, and read Packt's entire library of books.
Why subscribe?
• Fully searchable across every book published by Packt
• Copy and paste, print, and bookmark content
• On demand and accessible via a web browser
Free access for Packt account holders
If you have an account with Packt at www.PacktPub.com, you can use this to access
PacktLib today and view 9 entirely free books. Simply use your login credentials for
immediate access.
Table of Contents
Preface v
Chapter 1: Setting Up a Spark Virtual Environment 1
Understanding the architecture of
data-intensive applications 3
Infrastructure layer 4
Persistence layer 4
Integration layer 4
Analytics layer 5
Engagement layer 6
Understanding Spark 6
Spark libraries 7
PySpark in action 7
The Resilient Distributed Dataset 8
Understanding Anaconda 10
Setting up the Spark powered environment 12
Setting up an Oracle VirtualBox with Ubuntu 13
Installing Anaconda with Python 2.7 13
Installing Java 8 14
Installing Spark 15
Enabling IPython Notebook 16
Building our first app with PySpark 17
Virtualizing the environment with Vagrant 22
Moving to the cloud 24
Deploying apps in Amazon Web Services 24
Virtualizing the environment with Docker 24
Summary 26
[ i ]