Hadoop权威指南第三版英文原版详解

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"Hadoop权威指南第三版(英语原版)" 是一本由Tom White编写的关于Hadoop技术的详细教程，适合想要深入学习Hadoop的人群。本书由O'Reilly Media, Inc.出版，并在美国发行。在Hadoop领域，这本书被誉为权威指南，提供了全面且深入的Hadoop知识。第三版对原有的内容进行了更新，以适应Hadoop技术的最新发展。书中涵盖了从Hadoop的基础概念到高级应用的各个方面，包括分布式文件系统HDFS、MapReduce计算模型、YARN资源管理和调度器、Hadoop生态系统中的相关工具（如Hive、Pig、HBase等）以及数据处理和分析的最佳实践。早期发布修订1在2012年1月27日完成，而第一版正式发布于2012年5月7日。读者可以通过O'Reilly的错误报告页面获取关于该书的修正和更新信息。此书的编辑团队由Mike Loukides和Meghan Blanchette担任，生产编辑是Rachel Steely，副本编辑由Genevieve d'Entremont负责，校对员是Kevin Broccoli，索引编制同样由Kevin Broccoli完成。封面设计由Karen Montgomery创作，内页设计由David Futato完成，插图则由Robert Romano绘制。通过阅读这本书，读者可以了解到以下关键知识点： 1. **Hadoop分布式文件系统(HDFS)**：理解HDFS的基本架构，包括NameNode和DataNode的角色，以及HDFS的数据冗余和容错机制。 2. **MapReduce编程模型**：学习如何编写Map和Reduce任务，理解shuffle和sort过程，以及如何优化MapReduce作业的性能。 3. **YARN（Yet Another Resource Negotiator）**：了解YARN作为新一代的Hadoop资源管理框架，如何提供更细粒度的资源调度和应用程序管理。 4. **Hadoop生态系统的组件**：涵盖如Hive（基于SQL的查询工具）、Pig（数据流处理语言）和HBase（分布式NoSQL数据库）等工具的用法和应用场景。 5. **大数据处理最佳实践**：学习如何进行数据导入导出、数据清洗、数据存储策略以及大规模数据处理中的性能调优。 6. **实时处理与流计算**：探讨如Flume和Storm等用于实时数据处理的工具和技术。 7. **安全性与认证**：介绍Hadoop的安全模型，包括Kerberos认证和Hadoop的访问控制列表（ACLs）。 8. **Hadoop的扩展与升级**：讨论如何扩展Hadoop集群，以及在不中断服务的情况下进行软件升级。通过深入阅读和理解《Hadoop权威指南第三版》，读者不仅可以掌握Hadoop的核心技术，还能获得在实际项目中部署和管理Hadoop集群所需的知识和技能。

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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