HBase实战全书：大数据分析的利器 - CSDN文库

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"HBase实战全书，由Nick Dimiduk和Amandeep Khurana合著，技术编辑Mark Henry Ryan，是Manning出版社出版的一本深入探讨HBase的书籍，被誉为Hadoop中的列数据库权威指南，对于大数据分析至关重要。本书提供在线信息查询和订购，并在批量购买时提供折扣。" 《HBase实战全书》是大数据领域的一本经典著作，全面讲解了Apache HBase——一个基于Hadoop的数据存储系统。HBase作为NoSQL数据库的一种，尤其适用于处理海量半结构化或非结构化数据。它构建于Hadoop之上，利用HDFS（Hadoop分布式文件系统）作为底层存储，提供了高效、可伸缩的随机访问能力。书中的内容可能涵盖了以下几个核心知识点： 1. **HBase基础**：介绍HBase的基本概念，如表、行、列族、时间戳等，以及如何设计适合HBase的数据模型。 2. **HBase架构**：解析HBase的Master-Slave架构，包括RegionServer、Zookeeper的角色，以及数据分布和负载均衡策略。 3. **数据模型与API**：详细讲解如何创建、读取、更新和删除（CRUD）HBase表，以及使用Java API和其他语言客户端进行操作。 4. **性能优化**：探讨如何调整HBase配置以提高性能，包括Region大小、Split策略、Compaction机制等。 5. **HBase与Hadoop集成**：阐述如何在Hadoop生态系统中使用HBase，如与MapReduce、YARN的交互，以及HBase上的批处理和实时分析。 6. **故障恢复与高可用性**：讲解HBase的容错机制，包括Region Server的故障切换和数据恢复。 7. **监控与管理**：介绍如何监控HBase集群的健康状态，使用HBase自带的命令行工具和第三方工具进行管理和调优。 8. **实战案例**：提供实际应用示例，展示HBase在互联网、电信、广告、日志分析等领域的应用场景和最佳实践。这本书不仅适合对大数据感兴趣的初学者，也适合希望深入了解HBase高级特性和优化技巧的开发者和管理员。通过深入学习和实践，读者将能够掌握如何有效地利用HBase处理大规模数据挑战，实现高效的数据存储和分析。

xv

preface

I got my start with

HB

ase in the fall of 2008. It was a young project then, released only

in the preceding year. As early releases go, it was quite capable, although not without its

fair share of embarrassing warts. Not bad for an Apache subproject with fewer than 10

active committers to its name! That was the height of the No

SQL

hype. The term No

SQL

hadn’t even been presented yet but would come into common parlance over the next

year. No one could articulate why the idea was important—only that it was important—

and everyone in the open source data community was obsessed with this concept. The

community was polarized, with people either bashing relational databases for their fool-

ish rigidity or mocking these new technologies for their lack of sophistication.

The people exploring this new idea were mostly in internet companies, and I came

to work for such a company—a startup interested in the analysis of social media con-

tent. Facebook still enforced its privacy policies then, and Twitter wasn’t big enough to

know what a Fail Whale was yet. Our interest at the time was mostly in blogs. I left a

company where I’d spent the better part of three years working on a hierarchical data-

base engine. We made extensive use of Berkeley

DB

, so I was familiar with data tech-

nologies that didn’t have a

SQL

engine. I joined a small team tasked with building a

new data-management platform. We had an

MS

SQL

database stuffed to the gills with

blog posts and comments. When our daily analysis jobs breached the 18-hour mark,

we knew the current system’s days were numbered.

After cataloging a basic set of requirements, we set out to find a new data technol-

ogy. We were a small team and spent months evaluating different options while main-

taining current systems. We experimented with different approaches and learned

PREFACE

xvi

firsthand the pains of manually partitioning data. We studied the

CAP

theorem and

eventual consistency—and the tradeoffs. Despite its warts, we decided on

HB

ase, and

we convinced our manager that the potential benefits outweighed the risks he saw in

open source technology.

I’d played a bit with Hadoop at home but had never written a real MapReduce job.

I’d heard of

HB

ase but wasn’t particularly interested in it until I was in this new posi-

tion. With the clock ticking, there was nothing to do but jump in. We scrounged up a

couple of spare machines and a bit of rack, and then we were off and running. It was a

.

NET

shop, and we had no operational help, so we learned to combine bash with rsync

and managed the cluster ourselves.

I joined the mailing lists and the

IRC

channel and started asking questions. Around

this time, I met Amandeep. He was working on his master’s thesis, hacking up

HB

ase

to run on systems other than Hadoop. Soon he finished school, joined Amazon, and

moved to Seattle. We were among the very few HBase-ers in this extremely Microsoft-

centric city. Fast-forward another two years…

The idea of

HB

ase in Action was first proposed to us in the fall of 2010. From my

perspective, the project was laughable. Why should we, two community members,

write a book about

HB

ase? Internally, it’s a complex beast. The Definitive Guide was still

a work in progress, but we both knew its author, a committer, and were well aware of

the challenge before him. From the outside, I thought it’s just a “simple key-value

store.” The

API

has only five concepts, none of which is complex. We weren’t going to

write another internals book, and I wasn’t convinced there was enough going on from

the application developer’s perspective to justify an entire book.

We started brainstorming the project, and it quickly became clear that I was wrong.

Not only was there enough material for a user’s guide, but our position as community

members made us ideal candidates to write such a book. We set out to catalogue the

useful bits of knowledge we’d each accumulated over the couple of years we’d used

the technology. That effort—this book—is the distillation of our eight years of com-

bined

HB

ase experience. It’s targeted to those brand new to

HB

ase, and it provides

guidance over the stumbling blocks we encountered during our own journeys. We’ve

collected and codified as much as we could of the tribal knowledge floating around

the community. Wherever possible, we prefer concrete direction to vague advice. Far

more than a simple

FAQ

, we hope you’ll find this book to be a complete manual to

getting off the ground with

HB

ase.

HB

ase is now stabilizing. Most of the warts we encountered when we began with the

project have been cleaned up, patched, or completely re-architected.

HB

ase is

approaching its 1.0 release, and we’re proud to be part of this community as we

approach this milestone. We’re proud to present this manuscript to the community in

hopes that it will encourage and enable the next generation of HBase users. The sin-

gle strongest component of

HB

ase is its thriving community—we hope you’ll join us in

that community and help it continue to innovate in this new era of data systems.

N

ICK

D

IMIDUK

PREFACE

xvii

If you’re reading this, you’re presumably interested in knowing how I got involved

with

HB

ase. Let me start by saying thank you for choosing this book as your means to

learn about

HB

ase and how to build applications that use

HB

ase as their underlying

storage system. I hope you’ll find the text useful and learn some neat tricks that will

help you build better applications and enable you to succeed.

I was pursuing graduate studies in computer science at

UC

Santa Cruz, specializing

in distributed systems, when I started working at Cisco as a part-time researcher. The

team I was working with was trying to build a data-integration framework that could

integrate, index, and allow exploration of data residing in hundreds of heterogeneous

data stores, including but not limited to large

RDBMS

systems. We started looking for

systems and solutions that would help us solve the problems at hand. We evaluated

many different systems, from object databases to graph databases, and we considered

building a custom distributed data-storage layer backed by Berkeley

DB

. It was clear

that one of the key requirements was scalability, and we didn’t want to build a full-

fledged distributed system. If you’re in a situation where you think you need to build

out a custom distributed database or file system, think again—try to see if an existing

solution can solve part of your problem.

Following that principle, we decided that building out a new system wasn’t the best

approach and to use an existing technology instead. That was when I started playing

with the Hadoop ecosystem, getting my hands dirty with the different components in

the stack and going on to build a proof-of-concept for the data-integration system on

top of

HB

ase. It actually worked and scaled well!

HB

ase was well-suited to the problem,

but these were young projects at the time—and one of the things that ensured our

success was the community.

HB

ase has one of the most welcoming and vibrant open

source communities; it was much smaller at the time, but the key principles were the

same then as now.

The data-integration project later became my master’s thesis. The project used

HB

ase at its core, and I became more involved with the community as I built it out. I

asked questions, and, with time, answered questions others asked, on both the mailing

lists and the

IRC

channel. This is when I met Nick and got to know what he was work-

ing on. With each day that I worked on this project, my interest and love for the tech-

nology and the open source community grew, and I wanted to stay involved.

After finishing grad school, I joined Amazon in Seattle to work on back-end distrib-

uted systems projects. Much of my time was spent with the Elastic MapReduce team,

building the first versions of their hosted

HB

ase offering. Nick also lived in Seattle,

and we met often and talked about the projects we were working on. Toward the end

of 2010, the idea of writing HBase in Action for Manning came up. We initially scoffed

at the thought of writing a book on

HB

ase, and I remember saying to Nick, “It’s gets,

puts, and scans—there’s not a lot more to

HB

ase from the client side. Do you want to

write a book about three

API

calls?”

But the more we thought about this, the more we realized that building applications

with

HB

ase was challenging and there wasn’t enough material to help people get off the

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