Hadoop指南第三版：深入探索YARN与大数据处理

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"Hadoop 指南第三版本深入解析了 Apache Hadoop 及其生态系统，特别是对 YARN 有详尽的介绍。本书由 Tom White 编写，旨在为读者提供 Hadoop 的全面理解，包括 MapReduce 和 HDFS 的核心概念、实际应用以及最新发展技术。" 在《Hadoop：权威指南》第三版中，Tom White 阐述了 Hadoop 在数据存储和分析领域的应用，与传统关系型数据库管理系统（RDBMS）、网格计算以及志愿计算的对比。书中通过回顾 Hadoop 的发展历程，介绍了 Apache Hadoop 项目及其生态系统，并对各个版本的特性进行了概述。 MapReduce 是 Hadoop 的核心计算框架，书中通过一个天气数据集的例子，展示了如何使用 MapReduce 分析大量数据。MapReduce 的工作原理包括 Map 和 Reduce 两个阶段，以及数据处理流程中的 Combiner 函数。书中还详细介绍了如何扩展 MapReduce 以适应大规模数据处理，以及如何运行分布式 MapReduce 作业。此外，还涵盖了使用 Hadoop Streaming 进行编程，支持如 Ruby 和 Python 等多种语言。 Hadoop 分布式文件系统（HDFS）是另一个关键组成部分，书中详细探讨了 HDFS 的设计理念和主要概念。HDFS 旨在提供高容错性和高吞吐量的数据访问，支持大数据的存储。书中详细讲解了 NameNode、DataNode、Block、Replication 等 HDFS 核心概念，并且讨论了 HDFS 的容错机制和故障恢复策略。此外，书中还涉及了 YARN（Yet Another Resource Negotiator），这是 Hadoop 2.x 中引入的资源管理和调度系统，旨在提高集群资源利用率和系统的可扩展性。YARN 将资源管理和应用程序管理分离，使得 Hadoop 能够支持更多种类的计算框架，如 Spark 和 Tez。《Hadoop：权威指南》第三版是理解 Hadoop 生态系统、掌握 MapReduce 和 HDFS 技术的宝贵资料，适合数据工程师、大数据分析师以及希望深入了解大数据处理的读者。书中不仅提供了理论知识，还有丰富的实践示例，帮助读者将理论应用于实际工作场景。

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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