Hadoop权威指南：深入解析Hadoop开发与使用

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"HADOOP HIVE - Hadoop开发技术权威指南源版，详细讲解了有关Hadoop的开发使用" 在IT行业中，Hadoop是大数据处理领域的重要组成部分，它提供了一个分布式计算框架，允许在大规模集群上高效处理海量数据。本指南的作者Tom White是一位Hadoop专家，他的著作《Hadoop：权威指南》被广泛视为该领域的标准参考书。书中深入浅出地介绍了Hadoop的核心组件，包括HDFS（Hadoop Distributed File System）和MapReduce，以及与Hadoop生态系统相关的各种工具和服务。 HDFS是Hadoop的基础，它是一个分布式文件系统，设计目标是高容错性和高吞吐量的数据访问。HDFS将大文件分割成块，并在多台机器上复制这些块，以确保数据的可用性。这种设计使得Hadoop可以在廉价硬件上处理PB级别的数据。 MapReduce是Hadoop的并行计算模型，用于处理和生成大数据集。它分为两个阶段：Map阶段和Reduce阶段。Map阶段将输入数据拆分成键值对，然后在分布式环境中并行处理；Reduce阶段则聚合Map阶段的结果，进一步处理数据并生成最终输出。 Hive是Hadoop生态系统中的一个数据仓库工具，它允许用户使用SQL-like语言（称为HQL，Hive Query Language）来查询和管理存储在HDFS上的大型数据集。Hive提供了数据的结构化查询能力，简化了大数据分析的过程。它将SQL查询转换为一系列的MapReduce任务，使得非程序员也能方便地利用Hadoop进行数据分析。 Hadoop生态系统的其他重要组件包括Pig（一种高级数据流语言）、HBase（NoSQL数据库）、Zookeeper（分布式协调服务）和Spark（更快的批处理和实时处理框架）。这些工具共同构建了一个强大的数据处理平台，满足了现代企业对大数据分析的各种需求。《Hadoop：权威指南》不仅涵盖了Hadoop的基本概念和技术原理，还详细讨论了如何在实际项目中部署和优化Hadoop集群，以及如何使用Hive等工具进行数据分析。这本书对于希望深入了解和应用Hadoop的开发者、数据分析师以及IT专业人士来说，是一本不可多得的参考资料。

Given this, I was very pleased when I learned that Tom intended to write a book about

Hadoop. Who could be better qualified? Now you have the opportunity to learn about

Hadoop from a master—not only of the technology, but also of common sense and

plain talk.

—Doug Cutting

Shed in the Yard, California

xiv | Foreword

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

“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 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.

Chapters 11, 12, and 13 present Pig, HBase, and ZooKeeper, respectively.

Finally, Chapter 14 is a collection of case studies contributed by members of the Apache

Hadoop community.

xvi | Preface

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