深入理解分布式数据库内部机制：揭秘工作原理

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《数据库内幕》(DatabaseInternals)是一本由亚历克斯·彼得罗夫(Alex Petrov)编著的专业书籍，深入探讨了分布式数据系统的内部工作原理。该书以简洁明快的方式揭示了数据库技术的核心机制，让读者对存储、查询优化、并发控制、分布式架构等核心概念有深入理解。作者以其丰富的经验和深厚的专业知识，从底层出发，解释了数据库设计与实现背后的逻辑，包括SQL查询的执行流程、事务管理、索引结构的作用、分区和分片策略，以及在大型分布式系统中如何处理数据一致性问题。书中不仅涵盖了传统的关系型数据库，还可能涉及NoSQL数据库和其他新兴技术的比较分析。《数据库内幕》在全球多个城市如波士顿、法纳姆(Farnham)、 Sebastopol (本书出版地)、东京和北京发行，其国际版号为978-1-492-04034-7。这本书的版权由Oleksandr Petrov所有，于2019年首次出版。O'Reilly Media, Inc. 负责出版和发行，读者可以通过印刷版或在线版获取，适合教育、商业或销售推广用途。对于有兴趣进一步了解数据库技术的读者来说，该书提供了详细的代码示例和理论讲解，帮助读者提升技能，解决实际工作中遇到的问题。此外，书中还包含了首次发布后的修订历史，便于跟踪更新内容。如果你是数据库管理员、开发者或者对技术趋势感兴趣的人，这本深入的内部揭秘将是你不可或缺的学习资料。通过阅读《数据库内幕》，你不仅能掌握数据库技术的坚实基础，还能了解到在快速发展的技术环境中，如何设计和优化高效、可扩展的分布式数据库系统。这是一本值得反复研读并应用于实践的IT专业书籍。

Today, the field is changing again: after the time of key-value stores, NoSQL, and

eventual consistency, we have started seeing more scalable and performant databases,

able to execute complex queries with stronger consistency guarantees.

Audience of This Book

In conversations at technical conferences, I often hear the same question: “How can I

learn more about database internals? I don’t even know where to start.” Most of the

books on database systems do not go into details of storage engine implementation,

and cover the access methods, such as B-Trees, on a rather high level. There are very

few books that cover more recent concepts, such as different B-Tree variants and log-

structured storage, so I usually recommend reading papers.

Everyone who reads papers knows that it’s not that easy: you often lack context, the

wording might be ambiguous, there’s little or no connection between papers, and

they’re hard to find. This book contains concise summaries of important database

systems concepts and can serve as a guide for those who’d like to dig in deeper, or as a

cheat sheet for those already familiar with these concepts.

Not everyone wants to become a database developer, but this book will help people

who build software that uses database systems: software developers, reliability engi‐

neers, architects, and engineering managers.

If your company depends on any infrastructure component, be it a database, a mes‐

saging queue, a container platform, or a task scheduler, you have to read the project

change-logs and mailing lists to stay in touch with the community and be up-to-date

with the most recent happenings in the project. Understanding terminology and

knowing what’s inside will enable you to yield more information from these sources

and use your tools more productively to troubleshoot, identify, and avoid potential

risks and bottlenecks. Having an overview and a general understanding of how data‐

base systems work will help in case something goes wrong. Using this knowledge,

you’ll be able to form a hypothesis, validate it, find the root cause, and present it to

other project maintainers.

This book is also for curious minds: for the people who like learning things without

immediate necessity, those who spend their free time hacking on something fun, cre‐

ating compilers, writing homegrown operating systems, text editors, computer

games, learning programming languages, and absorbing new information.

The reader is assumed to have some experience with developing backend systems and

working with database systems as a user. Having some prior knowledge of different

data structures will help to digest material faster.

xiv | Preface

Why Should I Read This Book?

We often hear people describing database systems in terms of the concepts and algo‐

rithms they implement: “This database uses gossip for membership propagation” (see

Chapter 12), “They have implemented Dynamo,” or “This is just like what they’ve

described in the Spanner paper” (see Chapter 13). Or, if you’re discussing the algo‐

rithms and data structures, you can hear something like “ZAB and Raft have a lot in

common” (see Chapter 14), “Bw-Trees are like the B-Trees implemented on top of log

structured storage” (see Chapter 6), or “They are using sibling pointers like in B

link

Trees” (see Chapter 5).

We need abstractions to discuss complex concepts, and we can’t have a discussion

about terminology every time we start a conversation. Having shortcuts in the form

of common language helps us to move our attention to other, higher-level problems.

One of the advantages of learning the fundamental concepts, proofs, and algorithms

is that they never grow old. Of course, there will always be new ones, but new algo‐

rithms are often created after finding a flaw or room for improvement in a classical

one. Knowing the history helps to understand differences and motivation better.

Learning about these things is inspiring. You see the variety of algorithms, see how

our industry was solving one problem after the other, and get to appreciate that work.

At the same time, learning is rewarding: you can almost feel how multiple puzzle

pieces move together in your mind to form a full picture that you will always be able

to share with others.

Scope of This Book

This is neither a book about relational database management systems nor about

NoSQL ones, but about the algorithms and concepts used in all kinds of database sys‐

tems, with a focus on a storage engine and the components responsible for

distribution.

Some concepts, such as query planning, query optimization, scheduling, the rela‐

tional model, and a few others, are already covered in several great textbooks on data‐

base systems. Some of these concepts are usually described from the user’s

perspective, but this book concentrates on the internals. You can find some pointers

to useful literature in the Part II Conclusion and in the chapter summaries. In these

books you’re likely to find answers to many database-related questions you might

have.

Query languages aren’t discussed, since there’s no single common language among

the database systems mentioned in this book.

To collect material for this book, I studied over 15 books, more than 300 papers,

countless blog posts, source code, and the documentation for several open source

Preface | xv

databases. The rule of thumb for whether or not to include a particular concept in the

book was the question: “Do the people in the database industry and research circles

talk about this concept?” If the answer was “yes,” I added the concept to the long list

of things to discuss.

Structure of This Book

There are some examples of extensible databases with pluggable components (such as

[SCHWARZ86]), but they are rather rare. At the same time, there are plenty of exam‐

ples where databases use pluggable storage. Similarly, we rarely hear database vendors

talking about query execution, while they are very eager to discuss the ways their

databases preserve consistency.

The most significant distinctions between database systems are concentrated around

two aspects: how they store and how they distribute the data. (Other subsystems can

at times also be of importance, but are not covered here.) The book is arranged into

parts that discuss the subsystems and components responsible for storage (Part I) and

distribution (Part II).

Part I discusses node-local processes and focuses on the storage engine, the central

component of the database system and one of the most significant distinctive factors.

First, we start with the architecture of a database management system and present

several ways to classify database systems based on the primary storage medium and

layout.

We continue with storage structures and try to understand how disk-based structures

are different from in-memory ones, introduce B-Trees, and cover algorithms for effi‐

ciently maintaining B-Tree structures on disk, including serialization, page layout,

and on-disk representations. Later, we discuss multiple variants to illustrate the power

of this concept and the diversity of data structures influenced and inspired by B-

Trees.

Last, we discuss several variants of log-structured storage, commonly used for imple‐

menting file and storage systems, motivation, and reasons to use them.

Part II is about how to organize multiple nodes into a database cluster. We start with

the importance of understanding the theoretical concepts for building fault-tolerant

distributed systems, how distributed systems are different from single-node applica‐

tions, and which problems, constraints, and complications we face in a distributed

environment.

After that, we dive deep into distributed algorithms. Here, we start with algorithms

for failure detection, helping to improve performance and stability by noticing and

reporting failures and avoiding the failed nodes. Since many algorithms discussed

later in the book rely on understanding the concept of leadership, we introduce sev‐

eral algorithms for leader election and discuss their suitability.

xvi | Preface

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