Hadoop权威指南第二版：实战与核心技术详解

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《Hadoop权威指南》第二版（Hadoop: The Definitive Guide, Second Edition）是由Tom White编著的一本经典之作，它是Hadoop技术领域的基石，全面深入地介绍了这个开源框架。本书在2011年出版，随着大数据时代的兴起，Hadoop作为分布式计算和存储平台的重要性日益凸显，第二版的发布是对初版的更新和完善。本书由前言作者Doug Cutting撰写，强调了Hadoop技术的核心理念和发展历程，以及它在大数据处理中的关键作用。书中涵盖了Hadoop的各个组件，包括Hadoop Distributed File System (HDFS)、MapReduce编程模型、YARN资源调度器、HBase分布式列式数据库等，这些组件共同构建了一个强大的大数据处理生态系统。在Hadoop的使用场景上，第二版可能增添了对实时流处理、机器学习和大数据分析的探讨，因为这些是现代数据处理中的热门话题。此外，书中还可能包含了Hadoop的最佳实践、性能优化、集群管理和故障恢复等内容，帮助读者理解和应对实际部署中的挑战。对于那些希望深入理解并掌握Hadoop的开发者、数据工程师和分析师来说，《Hadoop权威指南》第二版是一本不可或缺的参考书。无论是初学者还是经验丰富的专业人员，都能从中获得最新的技术细节和实战指导。该书不仅适合自学，也常被用作大学课程和企业培训的教材。值得注意的是，该书的版权信息表明，O'Reilly Media Inc. 是出版社，提供了纸质版和在线版的选择，读者可以通过访问 www.allitebooks.com 下载或购买电子版。同时，作者保留所有权利，并且指出了本书的印刷历史，包括首次出版时间和第二版的更新时间。总结起来，《Hadoop权威指南》第二版是一部详尽的指南，旨在帮助读者全面掌握Hadoop技术，为大数据时代的数据处理提供扎实的基础。无论是技术原理、应用场景还是实战操作，本书都为读者提供了丰富的资源和深入的学习材料。

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577

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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