Hadoop入门与实践：权威指南第三版解析

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"Hadoop权威指南第三版，作者Tom White，由O'Reilly Media出版，包含HDFS、Hive、MapReduce、HBase和Sqoop等内容，是Hadoop入门的重要参考资料。" 《Hadoop权威指南》第三版是Tom White的经典著作，全面介绍了分布式计算框架Hadoop及其生态系统中的关键组件。这本书对于想要深入理解Hadoop技术体系的人来说，是一本不可或缺的读物。 Hadoop Distributed File System (HDFS) 是Hadoop的核心部分，它设计用于存储大规模数据集，具有高容错性和可扩展性。书中详细阐述了HDFS的数据模型、命名空间管理、数据块复制机制以及故障恢复策略，帮助读者理解如何在分布式环境中高效地存储和访问数据。 MapReduce是Hadoop处理大数据的主要计算模型，它将复杂计算任务分解为两个阶段：Map阶段和Reduce阶段。书中详细解释了MapReduce的工作原理、编程模型、作业调度和数据本地化优化，有助于开发者编写高效的MapReduce程序。 Hive是基于Hadoop的数据仓库工具，允许用户使用SQL-like语言（HQL）进行数据查询和分析。书中介绍了Hive的架构、表和分区设计、查询优化以及与外部数据源的集成，让读者了解如何在Hadoop上构建数据分析系统。 HBase是一个非关系型分布式数据库（NoSQL），基于HDFS，提供实时的数据访问。书中详细讨论了HBase的表模型、数据模型、读写操作以及与MapReduce的集成，为读者提供了在大数据场景下构建高性能数据存储系统的知识。 Sqoop则是一个工具，用于在Hadoop和传统的关系型数据库管理系统（RDBMS）之间导入导出数据。书中讲述了Sqoop的设计目标、使用方法和性能优化技巧，帮助读者实现大数据与结构化数据之间的无缝迁移。此外，书中还涵盖了YARN（Yet Another Resource Negotiator），它是Hadoop 2.x版本中的资源管理框架，负责管理和调度集群中的计算资源。YARN的引入改进了Hadoop的多应用支持和资源利用率。《Hadoop权威指南第三版》不仅介绍了Hadoop的基本概念和技术细节，还深入探讨了实际应用中的最佳实践和常见问题，是学习和掌握Hadoop不可或缺的参考资料。无论是初学者还是有经验的开发人员，都能从中受益匪浅，提升在大数据领域的专业技能。

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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