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Parallel R
Q. Ethan McCallum and Stephen Weston
Beijing Cambridge Farnham Köln Sebastopol Tokyo
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Parallel R
by Q. Ethan McCallum and Stephen Weston
Copyright © 2012 Q. Ethan McCallum and Stephen Weston. All rights reserved.
Printed in the United States of America.
Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.
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Revision History for the First Edition:
2011-10-21 First release
See http://oreilly.com/catalog/errata.csp?isbn=9781449309923 for release details.
Nutshell Handbook, the Nutshell Handbook logo, and the O’Reilly logo are registered trademarks of
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no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein.
ISBN: 978-1-449-30992-3
[LSI]
1319202138
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Table of Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
1. Getting Started . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Why R? 1
Why Not R? 1
The Solution: Parallel Execution 2
A Road Map for This Book 2
What We’ll Cover 3
Looking Forward… 3
What We’ll Assume You Already Know 3
In a Hurry? 4
snow 4
multicore 4
parallel 4
R+Hadoop 4
RHIPE 5
Segue 5
Summary 5
2. snow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Quick Look 7
How It Works 7
Setting Up 8
Working with It 9
Creating Clusters with makeCluster 9
Parallel K-Means 10
Initializing Workers 12
Load Balancing with clusterApplyLB 13
Task Chunking with parLapply 15
Vectorizing with clusterSplit 18
Load Balancing Redux 20
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Functions and Environments 23
Random Number Generation 25
snow Configuration 26
Installing Rmpi 29
Executing snow Programs on a Cluster with Rmpi 30
Executing snow Programs with a Batch Queueing System 32
Troubleshooting snow Programs 33
When It Works… 35
…And When It Doesn’t 36
The Wrap-up 36
3. multicore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Quick Look 37
How It Works 38
Setting Up 38
Working with It 39
The mclapply Function 39
The mc.cores Option 39
The mc.set.seed Option 40
Load Balancing with mclapply 42
The pvec Function 42
The parallel and collect Functions 43
Using collect Options 44
Parallel Random Number Generation 46
The Low-Level API 47
When It Works… 49
…And When It Doesn’t 49
The Wrap-up 49
4. parallel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Quick Look 52
How It Works 52
Setting Up 52
Working with It 53
Getting Started 53
Creating Clusters with makeCluster 54
Parallel Random Number Generation 55
Summary of Differences 57
When It Works… 58
…And When It Doesn’t 58
The Wrap-up 58
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5. A Primer on MapReduce and Hadoop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
Hadoop at Cruising Altitude 59
A MapReduce Primer 60
Thinking in MapReduce: Some Pseudocode Examples 61
Calculate Average Call Length for Each Date 62
Number of Calls by Each User, on Each Date 62
Run a Special Algorithm on Each Record 63
Binary and Whole-File Data: SequenceFiles 63
No Cluster? No Problem! Look to the Clouds… 64
The Wrap-up 66
6. R+Hadoop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Quick Look 67
How It Works 67
Setting Up 68
Working with It 68
Simple Hadoop Streaming (All Text) 69
Streaming, Redux: Indirectly Working with Binary Data 72
The Java API: Binary Input and Output 74
Processing Related Groups (the Full Map and Reduce Phases) 79
When It Works… 83
…And When It Doesn’t 83
The Wrap-up 84
7. RHIPE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Quick Look 85
How It Works 85
Setting Up 86
Working with It 87
Phone Call Records, Redux 87
Tweet Brevity 91
More Complex Tweet Analysis 96
When It Works… 98
…And When It Doesn’t 99
The Wrap-up 100
8. Segue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Quick Look 101
How It Works 102
Setting Up 102
Working with It 102
Model Testing: Parameter Sweep 102
When It Works… 105
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…And When It Doesn’t 105
The Wrap-up 106
9. New and Upcoming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
doRedis 107
RevoScale R and RevoConnectR (RHadoop) 108
cloudNumbers.com 108
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Preface
Conventions Used in This Book
The following typographical conventions are used in this book:
Italic
Indicates new terms, URLs, email addresses, filenames, and file extensions.
Constant width
Used for program listings, as well as within paragraphs to refer to program elements
such as variable or function names, databases, data types, environment variables,
statements, and keywords.
Constant width bold
Shows commands or other text that should be typed literally by the user.
Constant width italic
Shows text that should be replaced with user-supplied values or by values determined by context.
This icon signifies a tip, suggestion, or general note.
This icon indicates a warning or caution.
Using Code Examples
This book is here to help you get your job done. In general, you may use the code in
this book in your programs and documentation. You do not need to contact us for
permission unless you’re reproducing a significant portion of the code. For example,
writing a program that uses several chunks of code from this book does not require
permission. Selling or distributing a CD-ROM of examples from O’Reilly books does
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require permission. Answering a question by citing this book and quoting example
code does not require permission. Incorporating a significant amount of example code
from this book into your product’s documentation does require permission.
We appreciate, but do notrequire, attribution. An attribution usually includesthe title,
author, publisher, and ISBN. For example: “Parallel R by Q. Ethan McCallum and
Stephen Weston (O'Reilly). Copyright 2012 Q. Ethan McCallum and Stephen Weston,
978-1-449-30992-3.”
If you feel your use of code examplesfalls outside fair use orthe permission given above,
feel free to contact us at permissions@oreilly.com.
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Acknowledgments
There are only two names on the cover, but a host of people made this book possible.
We would like to thank the entire O’Reilly team for their efforts. They provided such
a smooth process that we were able to focus on just the writing. A special thanks goes
to our editors, Mike Loukides and Meghan Blanchette, for their guidance and support.
We would also like to thank our review team. The following people generously dedicated their time and energy to read this book in its early state, and their feedback helped
shape the text into the finished product you’re reading now:
Robert Bjornson
Nicholas Carriero
Jonathan Seidman
Paul Teetor
Ramesh Venkataramaiah
Jed Wing
Any errors you find in this book belong to us, the authors.
Most of all we thank you, the reader, for your interest in this book. We set out to create
the guidebook we wish we’d had when we first tried to give R that parallel, distributed
boost. R work is research work, best done with minimal distractions. We hope these
chapters help you get up to speed quickly, so you can get R to do what you need with
minimal detour from the task at hand.
Q. Ethan McCallum
“You like math? Oh, you need to talk to Mike. Let me introduce you.” I didn’t realize
it at the time, but those words were the start of this project. Really. A chance encounter
with Mike Loukides led to emails and phone calls and, before I knew it, we’d laid the
groundwork for a new book. So first and foremost, a hearty thanks to Betsy and Laurel,
who made my connection to Mike.
Conversations with Mike led me to my co-author, Steve Weston. I’m pleased and flattered that he agreed to join me on this adventure.
Thanks as well to the gang at Cafe les Deux Chats, for providing a quiet place to work.
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Stephen Weston
This was my first book project,so I’d like to thank my co-author and editorsfor putting
up with my freshman confusion and mistakes. They were very graciousthroughout the
project.
I’m very grateful to Nick, Rob, and Jed for taking the time to read my chapters and help
me not to make a fool of myself. I also want to thank my wife Diana and daughter Erica
for proofreading material that wasn’t on their preferred reading lists.
Finally, I’d like to thank all the authors of the packages that we discuss in this book. I
had a lot of fun reading the source for all three of the packages that I wrote about. In
particular, I’ve always loved the snow source code, which I studied when first learning
to program in R.
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