Hadoop权威指南第二版：深入解析大数据处理

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"Hadoop The Definitive Guide 2nd Edition" 《Hadoop The Definitive Guide》第二版是由Tom White撰写的一本关于Apache Hadoop的权威指南，这本书深入浅出地介绍了分布式计算框架Hadoop的核心概念、架构以及实际应用。作者Tom White是一位在大数据和Hadoop领域有深厚经验的技术专家，他的著作对初学者和专业人士来说都是极具价值的学习资源。 Hadoop是一个开源项目，最初由Doug Cutting创建，其灵感来源于Google的MapReduce和GFS（Google文件系统）论文。这本书的前言由Doug Cutting亲自撰写，进一步增强了其权威性。Hadoop的主要目标是处理和存储海量数据，它通过分布式计算模型使得处理大规模数据变得可能，尤其适合那些无法在单机上进行有效处理的数据集。本书第二版涵盖了自第一版以来Hadoop的许多新发展和改进，包括Hadoop 0.20系列的重大更新，以及Hadoop生态系统中其他组件如HDFS（Hadoop分布式文件系统）、MapReduce、HBase、Pig、Hive、ZooKeeper等的详细介绍。Tom White详细解释了Hadoop如何处理数据的输入和输出，如何进行数据分片和复制，以及如何确保数据的容错性。此外，书中还讨论了Hadoop的扩展性和灵活性，如何将Hadoop与其他数据存储系统集成，以及如何利用Hadoop进行数据分析。Tom White还分享了最佳实践，帮助读者理解和解决在部署和管理Hadoop集群时可能遇到的问题。书中的章节不仅包含了技术细节，还有大量的示例和实战案例，帮助读者更好地理解Hadoop的工作原理并应用于实际项目。读者可以通过这些实例学习如何编写MapReduce作业，如何设计高效的数据处理流程，以及如何优化Hadoop集群的性能。《Hadoop The Definitive Guide》第二版是一本全面且实用的Hadoop参考书籍，无论你是打算初次接触Hadoop，还是已经在使用Hadoop并希望深化理解，都能从中获益。书中的内容涵盖了从基础到高级的所有关键知识点，对于任何想要在大数据时代探索和利用Hadoop的人来说，都是不可或缺的参考资料。

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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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