Tom White's Hadoop权威教程(第3版)：数据处理与MapReduce详解

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《Hadoop权威教程》(Hadoop The Definitive Guide) 是由Tom White所著的英文原版第三版，专为读者提供深度理解和实践Hadoop技术的全面指南。本书在2012年进行了第一次修订，针对Hadoop及相关生态系统进行了详尽的介绍。Hadoop是一个开源的大数据处理框架，旨在解决海量数据的存储和并行计算问题，特别适用于大规模分布式环境。首先，本书从数据的角度出发，强调了数据存储和分析的重要性。Hadoop与传统的关系型数据库管理系统(RDBMS)如Oracle或MySQL相比，其优势在于能高效处理非结构化和半结构化数据，以及支持大规模数据的离线处理。此外，书中还提到了Hadoop与网格计算(Grid Computing)和志愿者计算(Volunteer Computing)的不同之处，展示了Hadoop在分布式计算中的独特定位。章节二深入探讨了MapReduce，Hadoop的核心编程模型，它包括两个主要阶段：Map和Reduce。作者通过实例演示，如分析天气数据集，展示了如何使用Unix工具和Hadoop自身的工具进行数据预处理和分析。MapReduce允许开发者编写简单的函数来处理数据，随着数据量的增加，通过“水平扩展”(Scaling Out)来分发任务到集群中的多个节点上，实现性能提升。书中进一步介绍了数据流(Data Flow)和组合器函数(Combiner Functions)，这些概念有助于优化MapReduce任务的效率。此外，除了Java，Hadoop还支持其他编程语言如Hadoop Streaming、Ruby和Python，使得开发者可以根据自己的需求选择合适的工具进行编程。第三部分着重讲解了Hadoop分布式文件系统(Hadoop Distributed File System, HDFS)，这是Hadoop架构的基础，用于存储大量数据。理解HDFS的工作原理对于有效管理Hadoop集群至关重要。编译和运行Hadoop程序的方法，如Hadoop Pipes，也在这一章中详细介绍。《Hadoop权威教程》提供了从入门到进阶的学习路径，涵盖了Hadoop的核心技术、开发实践和生态系统，适合于对大数据处理感兴趣的IT专业人士，无论是希望构建自己的Hadoop集群，还是在数据分析项目中运用Hadoop技术的开发者和数据科学家。无论是初学者还是经验丰富的用户，都能从中受益匪浅。

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.

xiii

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.

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.

xvi | Preface

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