Hadoop权威指南第二版：从入门到实践

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《Hadoop权威指南》第二版是由Tom White撰写的一本关于Apache Hadoop技术的经典著作。这本书在2011年首次出版，第二版于2010年10月发布，由O'Reilly Media公司发行，享有版权。作者Tom White以其深厚的行业背景和丰富的实践经验，为读者提供了对Hadoop生态系统全面且深入的理解。 Hadoop是一个开源的大数据处理框架，它基于Google的MapReduce计算模型和分布式文件系统HDFS（Hadoop Distributed File System）。该指南详细阐述了Hadoop的核心组件，包括Hadoop Distributed File System (HDFS)、MapReduce、YARN（Yet Another Resource Negotiator）以及Hadoop的其他扩展如HBase和Hive等。书中不仅介绍了如何安装、配置和管理Hadoop集群，还涵盖了数据处理的最佳实践、性能优化、故障恢复机制等内容。作为一本权威指南，它不仅适合Hadoop新手入门，也适合有经验的开发人员和管理员深入了解Hadoop体系结构和工作原理。本书的特色在于它紧密结合实际案例，帮助读者掌握如何在大规模分布式环境中设计和实现高效的数据处理解决方案。前言部分由 Doug Cutting撰写，他是Hadoop的创始人之一，为读者提供了Hadoop发展历史和其重要性的见解。封面设计独特，包含了非洲象的形象，象征着Hadoop在大数据领域中的力量和耐力。此外，本书还强调了在线资源的重要性，指出读者可以通过访问O'Reilly Safari Online平台获取更多相关技术更新和支持。编辑Mike Loukides和生产编辑Adam Zaremba共同确保了书籍的质量，而Proofreader Diane Il Grande则进行了细致的校对工作。Indexer Jay Book Services负责索引编排，使得信息查找更加方便。《Hadoop权威指南》第二版是Hadoop技术学习和发展历程中的重要参考资料，无论是开发者、数据科学家还是运维人员，都需要一本像这样详实且实用的指南来提升技能并应对大数据时代的挑战。通过阅读本书，读者可以深入理解Hadoop的架构、组件以及如何有效地在其中构建和运行大规模数据处理应用。

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xiv | Table of Contents

Foreword

Hadoop got its start in Nutch. A few of us were attempting to build an open source

web search engine and having trouble managing computations running on even a

handful of computers. Once Google published its GFS and MapReduce papers, the

route became clear. They’d devised systems to solve precisely the problems we were

having with Nutch. So we started, two of us, half-time, to try to re-create these systems

as a part of Nutch.

We managed to get Nutch limping along on 20 machines, but it soon became clear that

to handle the Web’s massive scale, we’d need to run it on thousands of machines and,

moreover, that the job was bigger than two half-time developers could handle.

Around that time, Yahoo! got interested, and quickly put together a team that I joined.

We split off the distributed computing part of Nutch, naming it Hadoop. With the help

of Yahoo!, Hadoop soon grew into a technology that could truly scale to the Web.

In 2006, Tom White started contributing to Hadoop. I already knew Tom through an

excellent article he’d written about Nutch, so I knew he could present complex ideas

in clear prose. I soon learned that he could also develop software that was as pleasant

to read as his prose.

From the beginning, Tom’s contributions to Hadoop showed his concern for users and

for the project. Unlike most open source contributors, Tom is not primarily interested

in tweaking the system to better meet his own needs, but rather in making it easier for

anyone to use.

Initially, Tom specialized in making Hadoop run well on Amazon’s EC2 and S3 serv-

ices. Then he moved on to tackle a wide variety of problems, including improving the

MapReduce APIs, enhancing the website, and devising an object serialization frame-

work. In all cases, Tom presented his ideas precisely. In short order, Tom earned the

role of Hadoop committer and soon thereafter became a member of the Hadoop Project

Management Committee.

Tom is now a respected senior member of the Hadoop developer community. Though

he’s an expert in many technical corners of the project, his specialty is making Hadoop

easier to use and understand.

Preface

Martin Gardner, the mathematics and science writer, once said in an interview:

Beyond calculus, I am lost. That was the secret of my column’s success. It took me so

long to understand what I was writing about that I knew how to write in a way most

readers would understand.

In many ways, this is how I feel about Hadoop. Its inner workings are complex, resting

as they do on a mixture of distributed systems theory, practical engineering, and com-

mon sense. And to the uninitiated, Hadoop can appear alien.

But it doesn’t need to be like this. Stripped to its core, the tools that Hadoop provides

for building distributed systems—for data storage, data analysis, and coordination—

are simple. If there’s a common theme, it is about raising the level of abstraction—to

create building blocks for programmers who just happen to have lots of data to store,

or lots of data to analyze, or lots of machines to coordinate, and who don’t have the

time, the skill, or the inclination to become distributed systems experts to build the

infrastructure to handle it.

With such a simple and generally applicable feature set, it seemed obvious to me when

I started using it that Hadoop deserved to be widely used. However, at the time (in

early 2006), setting up, configuring, and writing programs to use Hadoop was an art.

Things have certainly improved since then: there is more documentation, there are

more examples, and there are thriving mailing lists to go to when you have questions.

And yet the biggest hurdle for newcomers is understanding what this technology is

capable of, where it excels, and how to use it. That is why I wrote this book.

The Apache Hadoop community has come a long way. Over the course of three years,

the Hadoop project has blossomed and spun off half a dozen subprojects. In this time,

the software has made great leaps in performance, reliability, scalability, and manage-

ability. To gain even wider adoption, however, I believe we need to make Hadoop even

easier to use. This will involve writing more tools; integrating with more systems; and

* “The science of fun,” Alex Bellos, The Guardian, May 31, 2008, http://www.guardian.co.uk/science/

2008/may/31/maths.science.

xvii

writing new, improved APIs. I’m looking forward to being a part of this, and I hope

this book will encourage and enable others to do so, too.

Administrative Notes

During discussion of a particular Java class in the text, I often omit its package name,

to reduce clutter. If you need to know which package a class is in, you can easily look

it up in Hadoop’s Java API documentation for the relevant subproject, linked to from

the Apache Hadoop home page at http://hadoop.apache.org/. Or if you’re using an IDE,

it can help using its auto-complete mechanism.

Similarly, although it deviates from usual style guidelines, program listings that import

multiple classes from the same package may use the asterisk wildcard character to save

space (for example: import org.apache.hadoop.io.*).

The sample programs in this book are available for download from the website that

accompanies this book: http://www.hadoopbook.com/. You will also find instructions

there for obtaining the datasets that are used in examples throughout the book, as well

as further notes for running the programs in the book, and links to updates, additional

resources, and my blog.

What’s in This Book?

The rest of this book is organized as follows. Chapter 1 emphasizes the need for Hadoop

and sketches the history of the project. Chapter 2 provides an introduction to

MapReduce. Chapter 3 looks at Hadoop filesystems, and in particular HDFS, in depth.

Chapter 4 covers the fundamentals of I/O in Hadoop: data integrity, compression,

serialization, and file-based data structures.

The next four chapters cover MapReduce in depth. Chapter 5 goes through the practical

steps needed to develop a MapReduce application. Chapter 6 looks at how MapReduce

is implemented in Hadoop, from the point of view of a user. Chapter 7 is about the

MapReduce programming model, and the various data formats that MapReduce can

work with. Chapter 8 is on advanced MapReduce topics, including sorting and joining

data.

Chapters 9 and 10 are for Hadoop administrators, and describe how to set up and

maintain a Hadoop cluster running HDFS and MapReduce.

Later chapters are dedicated to projects that build on Hadoop or are related to it.

Chapters 11 and 12 present Pig and Hive, which are analytics platforms built on HDFS

and MapReduce, whereas Chapters 13, 14, and 15 cover HBase, ZooKeeper, and

Sqoop, respectively.

Finally, Chapter 16 is a collection of case studies contributed by members of the Apache

Hadoop community.

xviii | Preface

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