Hadoop权威指南第三版：新功能与升级详解

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《Hadoop权威指南》第三版英文版是一本针对Apache Hadoop开源分布式计算框架的详尽指南。该书于2012年5月出版，旨在更新读者对Hadoop 1.x（包括0.20系列）到0.23系列的最新知识，涵盖了这些版本的核心技术和新特性。作者Tom White确保所有示例在最新版本的Hadoop上运行，并特别提到了在新旧API之间的转换，以便帮助读者适应技术的演进。新版本的核心内容包括： 1. 新MapReduce API：书中大部分示例采用了新版本的MapReduce API，尽管旧API仍然存在，但新API成为主导，旧API的代码可以在书的官方网站上获取。这涉及到了新版本中的MapReduce 2 runtime，它是建立在分布式资源管理系统YARN之上的，作者详细解释了YARN的工作原理和使用方法。 2. 深入MapReduce实践：书中增加了对Maven打包MapReduce项目、Java环境配置、MRUnit单元测试以及输出提交器、分布式缓存等高级功能的讲解。此外，还专门介绍了如何处理Avro数据和使用Oozie进行简单工作流的运行。 3. HDFS增强：在第三章中，作者对HDFS的高可用性、联合特性以及WebHDFS和HttpFS进行了扩展介绍，这些都是Hadoop 0.23的重要更新。 4. 框架更新：章节关于Pig、Hive、Sqoop和ZooKeeper等工具的介绍都得到了扩充，反映了各自最新版本的功能和改进。 5. 改进与修订：作者对全书进行了大量的修正和提升，确保内容的准确性和实用性。这本书不仅适合Hadoop初学者，也是经验丰富的开发者的必备参考，帮助他们掌握Hadoop生态系统的关键技术和最佳实践。对于希望在大数据处理领域深入学习的读者来说，这是不可多得的权威指南。

Operations 593

Taps, Schemes, and Flows 594

Cascading in Practice 595

Flexibility 598

Hadoop and Cascading at ShareThis 599

Summary 603

TeraByte Sort on Apache Hadoop 603

Using Pig and Wukong to Explore Billion-edge Network Graphs 607

Measuring Community 609

Everybody’s Talkin’ at Me: The Twitter Reply Graph 609

Symmetric Links 612

Community Extraction 613

A. Installing Apache Hadoop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617

B. Cloudera’s Distribution Including Apache Hadoop . . . . . . . . . . . . . . . . . . . . . . . . . . 623

C. Preparing the NCDC Weather Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629

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

1. “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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