Hadoop实践指南：Alex Holmes的权威解析

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"Hadoop in Practice" 是一本由 Alex Holmes 编著的书籍，由 Manning Publications 出版。这本书深入浅出地介绍了 Hadoop 的实际应用，对于想在实践中掌握 Hadoop 的读者来说非常实用。 Hadoop 是一个开源的分布式计算框架，主要由 Apache 软件基金会维护。它最初设计的目标是处理和存储海量数据，尤其适合于那些无法在单台机器上处理的大型数据集。Hadoop 的核心组件包括 HDFS（Hadoop Distributed File System）和 MapReduce，这两个组件共同构建了一个可靠、可扩展的数据处理平台。《Hadoop in Practice》这本书涵盖了以下几个重要的知识点： 1. **Hadoop 系统架构**：书中详细讲解了 Hadoop 的基本架构，包括 NameNode、DataNode 和 JobTracker、TaskTracker 等组件的工作原理，以及它们如何协同工作以实现数据的分布式存储和处理。 2. **MapReduce 模型**：MapReduce 是 Hadoop 中用于并行处理大数据的核心算法模型。书中会介绍 Map 阶段和 Reduce 阶段的具体实现，以及如何编写 MapReduce 程序。 3. **HDFS 文件系统**：HDFS 是 Hadoop 的分布式文件系统，提供了高容错性和高吞吐量的数据访问。书中会解释 HDFS 的副本策略、数据块概念，以及如何优化 HDFS 的性能。 4. **Hadoop 安装与配置**：书中包含 Hadoop 的安装教程，包括伪分布式和完全分布式模式的设置，以及常见的配置参数调整。 5. **Hadoop 生态系统**：除了基础组件，Hadoop 生态系统还包括 Pig、Hive、HBase、Spark 等工具。书中可能会涵盖这些工具的基本使用和它们在大数据分析中的作用。 6. **数据处理与分析**：书中会介绍如何利用 Hadoop 进行数据清洗、转换和分析，以及如何处理不同类型的数据源，如日志文件、数据库等。 7. **实战案例**：作者 Alex Holmes 提供了丰富的实战示例，帮助读者理解 Hadoop 在实际业务场景中的应用，如推荐系统、日志分析等。 8. **故障排查与性能优化**：书中还会讨论 Hadoop 系统可能出现的问题及解决方法，以及如何通过监控和调优来提高系统的整体性能。《Hadoop in Practice》是一本全面而实用的 Hadoop 学习资料，无论是初学者还是有经验的开发人员，都能从中受益，提升自己在大数据领域的技能。通过阅读这本书，读者将能够更好地理解和应用 Hadoop 技术，解决实际工作中遇到的大数据挑战。

preface

I first encountered Hadoop in the fall of 2008 when I was working on an internet

crawl and analysis project at Verisign. My team was making discoveries similar to those

that Doug Cutting and others at Nutch had made several years earlier regarding how

to efficiently store and manage terabytes of crawled and analyzed data. At the time, we

were getting by with our home-grown distributed system, but the influx of a new data

stream and requirements to join that stream with our crawl data couldn’t be sup-

ported by our existing system in the required timelines.

After some research we came across the Hadoop project, which seemed to be a

perfect fit for our needs—it supported storing large volumes of data and provided a

mechanism to combine them. Within a few months we’d built and deployed a Map-

Reduce application encompassing a number of MapReduce jobs, woven together with

our own MapReduce workflow management system onto a small cluster of 18 nodes. It

was a revelation to observe our MapReduce jobs crunching through our data in min-

utes. Of course we couldn’t anticipate the amount of time that we’d spend debugging

and performance-tuning our MapReduce jobs, not to mention the new roles we took

on as production administrators—the biggest surprise in this role was the number of

disk failures we encountered during those first few months supporting production!

As our experience and comfort level with Hadoop grew, we continued to build

more of our functionality using Hadoop to help with our scaling challenges. We also

started to evangelize the use of Hadoop within our organization and helped kick-start

other projects that were also facing big data challenges.

The greatest challenge we faced when working with Hadoop (and specifically

MapReduce) was relearning how to solve problems with it. MapReduce is its own

ABOUT

THIS

BOOK

xix

Roadmap

This book has 13 chapters divided into five parts.

Part 1 contains a single chapter that’s the introduction to this book. It reviews

Hadoop basics and looks at how to get Hadoop up and running on a single host. It

wraps up with a walk-through on how to write and execute a MapReduce job.

Part 2, “Data logistics,” consists of two chapters that cover the techniques and

tools required to deal with data fundamentals, getting data in and out of Hadoop,

and how to work with various data formats. Getting data into Hadoop is one of the

first roadblocks commonly encountered when working with Hadoop, and chapter 2

is dedicated to looking at a variety of tools that work with common enterprise data

sources. Chapter 3 covers how to work with ubiquitous data formats such as

XML

and

JSON

in MapReduce, before going on to look at data formats better suited to

working with big data.

Part 3 is called “Big data patterns,” and looks at techniques to help you work effec-

tively with large volumes of data. Chapter 4 examines how to optimize MapReduce

join and sort operations, and chapter 5 covers working with a large number of small

files, and compression. Chapter 6 looks at how to debug MapReduce performance

issues, and also covers a number of techniques to help make your jobs run faster.

Part 4 is all about “Data science,” and delves into the tools and methods that help

you make sense of your data. Chapter 7 covers how to represent data such as graphs

for use with MapReduce, and looks at several algorithms that operate on graph data.

Chapter 8 describes how R, a popular statistical and data mining platform, can be inte-

grated with Hadoop. Chapter 9 describes how Mahout can be used in conjunction

with MapReduce for massively scalable predictive analytics.

Part 5 is titled “Taming the elephant,” and examines a number of technologies

that make it easier to work with MapReduce. Chapters 10 and 11 cover Hive and Pig

respectively, both of which are MapReduce domain-specific languages (DSLs) geared

at providing high-level abstractions. Chapter 12 looks at Crunch and Cascading, which

are Java libraries that offer their own MapReduce abstractions, and chapter 13 covers

techniques to help write unit tests, and to debug MapReduce problems.

The appendixes start with appendix A, which covers instructions on installing both

Hadoop and all the other related technologies covered in the book. Appendix B cov-

ers low-level Hadoop ingress/egress mechanisms that the tools covered in chapter 2

leverage. Appendix C looks at how

HDFS

supports reads and writes, and appendix D

covers a couple of MapReduce join frameworks written by the author and utilized in

chapter 4.

Code conventions and downloads

All source code in listings or in text is in a

fixed-width

font

this

to separate it

from ordinary text. Code annotations accompany many of the listings, highlighting

important concepts.

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