- Paperback: 528 pages
- Publisher: O'Reilly Media; 1 edition (June 15, 2009)
- Language: English
- ISBN-10: 0596521979
- ISBN-13: 978-0596521974
- Product Dimensions: 7 x 1 x 9.2 inches
- Shipping Weight: 1.6 pounds (View shipping rates and policies)
- Average Customer Review: 16 customer reviews
- Amazon Best Sellers Rank: #1,813,944 in Books (See Top 100 in Books)
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Hadoop: The Definitive Guide 1st Edition
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About the Author
Tom White has been an Apache Hadoop committer since February 2007, and is a member of the Apache Software Foundation. He works for Cloudera, a company set up to offer Hadoop support and training. Previously he was as an independent Hadoop consultant, working with companies to set up, use, and extend Hadoop. He has written numerous articles for O'Reilly, java.net and IBM's developerWorks, and has spoken at several conferences, including at ApacheCon 2008 on Hadoop. Tom has a Bachelor's degree in Mathematics from the University of Cambridge and a Master's in Philosophy of Science from the University of Leeds, UK.
Top customer reviews
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That being said, I was ultimately rather disappointed. First, and most importantly, it was not clear to me after reading this book how I might use Hadoop for some of my projects, or if indeed they were good candidates for MapReduce. I feel it should have been possible to provide some generic guidance. Second, some chapters are written by other authors, and these did not uniformly provide the same quality of instruction, reading occasionally like advertisements.
I confess I am puzzled by the number of encapsulating and utility APIs that have grown up around Hadoop. Why do we need Pig, HBase, Hive, Zookeeper and Cascading? Apparently because (according to what I have read here), bare Hadoop is hard to program with (productively). Some indication of how these wrappers interact with each other would have been helpful.
As it is, I feel LESS urge to evangelize for Hadoop having read this book. Surely not the desired effect?
Hadoop is the brainchild of Doug Cutting, who named his creation after his kid's stuffed yellow elephant. Hadoop enables large datasets distributed over a cluster of machines to be processed in parallel. One machine or node in that cluster would usually house a JobTracker and a NameNode. The JobTracker schedules and manages processing jobs to be executed in the other machines, and the NameNode manages the metadata (e.g., file names and locations, etc) of the datasets to be processed. The processing jobs are programmed in the form of Map and Reduce functions. Inputs are usually split into blocks to be processed in parallel by two or more identical mappers. The close to final outputs are then fed to one or more identical reducers, whose job is to perform any final transformations on the intermediate data to produce data summaries in the expected format. Several companies are using Hadoop to extract knowledge from their extensive data.
I've read this book and Jason Venners' Pro Hadoop book. Although I like both, I like this book better for the following reasons: more comprehensive coverage of topics, and more insiders' information on design rationales and how certain Hadoop features really work behind the scenes.
Here's a breakdown of and some commentaries on the book's contents:
Chapter One introduces Hadoop, its history and how it's different from similar tools or frameworks. Kinda dry. Chapter Two introduces the MapReduce Programming model and its benefits when compared to, say, the use of Unix tools for achieving parallel processing of text files. This is also where readers are introduced to the concepts of: map, combiner, and reduce functions, shuffle and sort, streaming, etc. Chapters Three and Four are all about the Hadoop Distributed FileSystems and I/O and the design decisions that were made to address performance, reliability, and safety concerns.
Chapter Five shows you how to develop, configure, test, run and tune a MapReduce Application. Good chapter but Jason Venner's book has better materials on testing and debugging MapReduce applications.
Chapters Six through Eight discuss how MapReduce really works behind the scene, including advanced features. This is where you'll learn how flexible Hadoop is when it comes to handling different types of inputs and outputs in terms of numbers, sizes, formats, and usage scenarios. Excellent!
Chapters Nine and Ten are really good. They teach you how to set up and administer Hadoop clusters. There's even a brief but informative section on how to use Hadoop with Amazon EC2 servers.
Chapters 11-13 devote one chapter each on how to install and interact with frameworks built on top of Hadoop: Pig, HBase, and ZooKeeper. Chapter 14 provides Case Studies (e.g., How Facebook uses Hadoop to analyze ad campaign effectiveness, etc.).
Appendices A and B provide instructions on how to install Apache's Hadoop and Cloudera's distribution, respectively, and C gives you a runthrough of the steps to take when preparing to use the NCDC Weather Data used in the book.
Very thorough and well written book. 4.5 stars rating.
Most recent customer reviews
The content was useful to those who want to learn the basics of hadoop programming and its...Read more
a) Get started running your own cluster and writing your own MR jobs
b) Understand how to administer the...Read more
Managing and analyzing huge data sets has become a very common problem in various areas of modern information...Read more
This book was my point of reference for setting up and testing up a small cluster.Read more