Manning《大数据实战指南》2015出版，Nathan Marz与James Warren合著

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《Manning.Big.Data》是一本于2015年4月出版的大数据经典著作，由Nathan Marz与James Warren合著。本书旨在深入讲解可扩展实时数据系统的原理和最佳实践，针对大数据领域提供了丰富的理论和实用指导。作为一本英文原版书籍，它将复杂的概念以易于理解的方式呈现，适合那些希望在大数据处理技术上提升专业技能的读者。书中的核心内容围绕着如何构建和管理大规模、高并发的数据系统，强调了在处理海量数据时的关键原则，如数据架构设计、分布式计算、流处理、实时分析以及数据存储优化等。作者们分享了他们在实际项目中的经验教训，探讨了如何在面对不断增长的数据量和性能需求时，保持系统的稳定性和效率。书中还涵盖了如何利用Hadoop、Spark、Storm等流行的开源工具和技术来实现数据的高效处理，并介绍了如何通过监控和调试技术来确保系统的正常运行。此外，书中还特别关注了实时数据处理中的挑战，如数据一致性、延迟控制以及如何在处理实时和批量作业之间取得平衡。对于那些寻求在大数据行业中立足的专业人士和开发者而言，《Manning.Big.Data》是一本不可多得的参考资料。它不仅提供了理论框架，还提供了许多实战案例，帮助读者在实际工作中应用所学知识，提升大数据处理能力。同时，由于其广泛的应用价值，这本书在企业级培训和团队协作中也具有很高的参考价值。版权方面，本书受Manning Publications Co.所有，未经出版商事先书面许可，任何形式的复制、存储或传输都是被禁止的。对于批量订购或获取更多优惠信息，读者可通过访问www.manning.com或直接联系出版社的特别销售部门获取详情。《Manning.Big.Data》是一本实用且权威的大数据指南，对于理解和解决现代IT环境中面临的复杂数据挑战，提供了宝贵的理论支持和实践经验。无论是初学者还是经验丰富的专业人士，都能从中受益匪浅。

acknowledgments

This book would not have been possible without the help and support of so many

individuals around the world. I must start with my parents, who instilled in me from a

young age a love of learning and exploring the world around me. They always encour-

aged me in all my career pursuits.

Likewise, my brother Iorav encouraged my intellectual interests from a young age.

I still remember when he taught me Algebra while I was in elementary school. He was

the one to introduce me to programming for the first time—he taught me Visual

Basic as he was taking a class on it in high school. Those lessons sparked a passion for

programming that led to my career.

I am enormously grateful to Michael Montano and Christopher Golda, the found-

ers of BackType. From the moment they brought me on as their first employee, I was

given an extraordinary amount of freedom to make decisions. That freedom was

essential for me to explore and exploit the Lambda Architecture to its fullest. They

never questioned the value of open source and allowed me to open source our tech-

nology liberally. Getting deeply involved with open source has been one of the great

privileges of my life.

Many of my professors from my time as a student at Stanford deserve special

thanks. Tim Roughgarden is the best teacher I’ve ever had—he radically improved my

ability to rigorously analyze, deconstruct, and solve difficult problems. Taking as many

classes as possible with him was one of the best decisions of my life. I also give thanks

to Monica Lam for instilling within me an appreciation for the elegance of Datalog.

Many years later I married Datalog with MapReduce to produce my first significant

open source project, Cascalog.

Licensed to Mark Watson <nordickan@gmail.com>

ACKNOWLEDGMENTS

xvi

Chris Wensel was the first one to show me that processing data at scale could be elegant

and performant. His Cascading library changed the way I looked at Big Data processing.

None of my work would have been possible without the pioneers of the Big Data field.

Special thanks to Jeffrey Dean and Sanjay Ghemawat for the original MapReduce paper,

Giuseppe DeCandia, Deniz Hastorun, Madan Jampani, Gunavardhan Kakulapati,

Avinash Lakshman, Alex Pilchin, Swaminathan Sivasubramanian, Peter Vosshall and

Werner Vogels for the original Dynamo paper, and Michael Cafarella and Doug Cutting

for founding the Apache Hadoop project.

Rich Hickey has been one of my biggest inspirations during my programming

career. Clojure is the best language I have ever used, and I’ve become a better pro-

grammer having learned it. I appreciate its practicality and focus on simplicity. Rich’s

philosophy on state and complexity in programming has influenced me deeply.

When I started writing this book, I was not nearly the writer I am now. Renae Gre-

goire, one of my development editors at Manning, deserves special thanks for helping

me improve as a writer. She drilled into me the importance of using examples to lead

into general concepts, and she set off many light bulbs for me on how to effectively

structure technical writing. The skills she taught me apply not only to writing techni-

cal books, but to blogging, giving talks, and communication in general. For gaining an

important life skill, I am forever grateful.

This book would not be nearly of the same quality without the efforts of my co-

author James Warren. He did a phenomenal job absorbing the theoretical concepts

and finding even better ways to present the material. Much of the clarity of the book

comes from his great communication skills.

My publisher, Manning, was a pleasure to work with. They were patient with me

and understood that finding the right way to write on such a big topic takes time.

Through the whole process they were supportive and helpful, and they always gave me

the resources I needed to be successful. Thanks to Marjan Bace and Michael Stephens

for all the support, and to all the other staff for their help and guidance along the way.

I try to learn as much as possible about writing from studying other writers. Brad-

ford Cross, Clayton Christensen, Paul Graham, Carl Sagan, and Derek Sivers have

been particularly influential.

Finally, I can’t give enough thanks to the hundreds of people who reviewed, com-

mented, and gave feedback on our book as it was being written. That feedback led us

to revise, rewrite, and restructure numerous times until we found ways to present the

material effectively. Special thanks to Aaron Colcord, Aaron Crow, Alex Holmes, Arun

Jacob, Asif Jan, Ayon Sinha, Bill Graham, Charles Brophy, David Beckwith, Derrick

Burns, Douglas Duncan, Hugo Garza, Jason Courcoux, Jonathan Esterhazy, Karl

Kuntz, Kevin Martin, Leo Polovets, Mark Fisher, Massimo Ilario, Michael Fogus,

Michael G. Noll, Patrick Dennis, Pedro Ferrera Bertran, Philipp Janert, Rodrigo

Abreu, Rudy Bonefas, Sam Ritchie, Siva Kalagarla, Soren Macbeth, Timothy Chk-

lovski, Walid Farid, and Zhenhua Guo.

ATHAN MARZ

Licensed to Mark Watson <nordickan@gmail.com>

ABOUT THIS BOOK

xix

concepts that hold true regardless of existing tools, while illustration chapters use

real-world tools to demonstrate the concepts. Don’t let the names fool you, though—

all chapters are highly example-driven.

Chapters 2 through 9 focus on the batch layer of the Lambda Architecture. Here you

will learn about modeling your master dataset, using batch processing to create arbitrary

views of your data, and the trade-offs between incremental and batch processing.

Chapters 10 and 11 focus on the serving layer, which provides low latency access to

the views produced by the batch layer. Here you will learn about specialized databases

that are only written to in bulk. You will discover that these databases are dramatically

simpler than traditional databases, giving them excellent performance, operational,

and robustness properties.

Chapters 12 through 17 focus on the speed layer, which compensates for the batch

layer’s high latency to provide up-to-date results for all queries. Here you will learn

about No

SQL databases, stream processing, and managing the complexities of incre-

mental computation.

Chapter 18 uses your new-found knowledge to review the Lambda Architecture

once more and fill in any remaining gaps. You’ll learn about incremental batch pro-

cessing, variants of the basic Lambda Architecture, and how to get the most out of

your resources.

Code downloads and conventions

The source code for the book can be found at https://github.com/Big-Data-Manning.

We have provided source code for the running example SuperWebAnalytics.com.

Much of the source code is shown in numbered listings. These listings are meant

to provide complete segments of code. Some listings are annotated to help highlight

or explain certain parts of the code. In other places throughout the text, code frag-

ments are used when necessary.

Courier

typeface

is used to denote code for Java. In

both the listings and fragments, we make use of a

bold

code

font

to help identify key

parts of the code that are being explained in the text.

Author Online

Purchase of Big Data includes free access to a private web forum run by Manning Pub-

lications where you can make comments about the book, ask technical questions, and

receive help from the authors and other users. To access the forum and subscribe to

it, point your web browser to www.manning.com/BigData. This Author Online (

AO)

page provides information on how to get on the forum once you’re registered, what

kind of help is available, and the rules of conduct on the forum.

Manning’s commitment to our readers is to provide a venue where a meaningful

dialog among individual readers and between readers and the authors can take place.

It’s not a commitment to any specific amount of participation on the part of the

authors, whose contribution to the

AO forum remains voluntary (and unpaid). We

suggest you try asking the authors some challenging questions, lest their interest stray!

Licensed to Mark Watson <nordickan@gmail.com>

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