Apache Hadoop详解：构建分布式系统

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"Hadoop: The Definitive Guide 第三版" Apache Hadoop 是一个开源框架，主要用于处理和存储大规模数据集。它基于Google的MapReduce算法设计，是构建可靠、可扩展分布式系统的首选工具。Hadoop的核心优势在于其能够处理PB级别的数据，使得企业能够高效地分析大量数据，从而挖掘潜在价值。本书《Hadoop: The Definitive Guide》由Tom White撰写，旨在帮助程序员和管理员深入理解并应用Hadoop。书中详细介绍了如何构建和管理Hadoop集群，以及如何利用Hadoop进行大数据分析。在对比其他系统部分，书中提到了关系型数据库管理系统（RDBMS）和网格计算，指出Hadoop在处理非结构化和半结构化数据方面具有优势，同时具备更好的可扩展性。与志愿者计算相比，Hadoop提供了一个更加集中管理和调度的工作模型，适合大规模的数据处理任务。 Hadoop生态系统包括多个组件，如HDFS（Hadoop Distributed File System）用于分布式存储，MapReduce则用于分布式计算。书中详细解释了Hadoop的历史和发展，以及不同版本之间的兼容性问题。 MapReduce是Hadoop中的关键概念，它通过将大任务分解成小任务（映射阶段）并在多台机器上并行处理，然后将结果合并（归约阶段）。书中以一个天气数据集为例，展示了如何使用Hadoop和传统的Unix工具对数据进行分析。Java MapReduce是实现MapReduce功能的主要方式，但为了满足多种编程语言的需求，Hadoop还提供了Hadoop Streaming接口，支持如Ruby和Python等脚本语言编写Mapper和Reducer。在介绍MapReduce的运行流程时，书中有提及Combiner函数，这是一种优化策略，能在本地节点上预先减少网络通信量。此外，书中还详细阐述了如何分布式执行MapReduce作业，并给出了实际操作的例子。《Hadoop: The Definitive Guide》是一本全面介绍Hadoop及其应用的指南，对于想要深入理解和应用Hadoop的读者来说，是一份不可或缺的参考材料。

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.

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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.

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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.

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