Relational databases are tricky beasts. Other kinds of commercial software are infinitely easier to understand. Word processors are really just high-tech typewriters, and it's pretty clear that the backspace key beats that little jar of white stuff cold. Spreadsheets present a familiar enough paradigm, even to non-accountants, and email is close enough to the postal system for the model to be comprehensible. Databases are different. Other kinds of software have a real-world analogy. Sometimes, as in the Windows desktop, the analogy is a little tenuous, but the analogies are close enough; you can get there from here. But relational databases are completely artificial. They're like geometry: They can be used to build models of the real world, but they don't exist in the real world. When was the last time you poured some wine for you and your sweetie and went out on the front porch to watch the geometry frolic on the lake? Now, I'm talking about databases here, not tables. Tables exist aplenty, from the telephone book to the dictionary. But relational databases? Nope. Uh-uh. You're not going to find them frolicking on the lake, either. The card files at the library, which contain author, title, and subject files, come close to being a database but they're still separate sets of data that are only correlated by the good graces of the local librarian. This book is about designing database systems. My intention is to give you the knowledge you need to take a messy, complex, real-world situation and turn it into an effective database design. I assume that you have some development experience and generally know your way around a computer, but I don't assume that you have any background in databases. After reading the book you still won't be able to watch the databases frolic on the lake, but if I've done my job well you'll be able to design and implement a relational model of the fish, the seagulls, and the effects of the plankton on them both. The book is divided into four parts. Part I, Relational Database Theory, covers the fundamental principles of the relational model. This is where the really ugly, theoretical stuff is. But don't worry; it will get easier. Part II, Dimensional Database Theory, covers the same information for dimensional databases, a special type of relational database used for analysis. Part III, Designing Database Systems, examines the analysis and design process-;what you should do to get from the real world to a reliable database system design. Finally, Part IV, Designing the User Interface, discusses the most important aspect of a database system from a user's point of view: the user interface. Although we'll talk about implementation issues in the next few hundred pages, this isn't a how to program book. There are a few coding examples, but I've kept them to a minimum, and you should be able to follow them even if you've never seen a programming language before. The database examples are based on the Northwind sample database that comes with Microsoft Access. (The version of Northwind that comes with SQL Server is very similar.) By the time you're finished reading this book, you'll have picked up most of what you need to get started building database systems, and you'll be ready to turn to one of the sources listed in the Bibliography for the finer points of programming style. And you'll be confident that your data architecture is sound and unlikely to get you into trouble later in your project. A note on English usage: As you'll discover as you read this book, I'm a stickler for terminology. But that said, I don't think syntax ought to draw attention to itself. If an author writes he or she or (heavens forefend) s/he, I'm busy thinking about gender politics and no longer paying attention to the text. If I read the data are, I'm just as likely to be thinking about the nature of the English language as whatever the author is trying to say. Now, the pronoun issue is fairly simple to work around. You'll find a great many repetitions of the user in this text. But the adoption of Latin terms into English is a more complex issue, particularly in a book about data. For the record, I had a classical education, and I'm perfectly aware that in Latin, data is a plural noun, and ought to take a plural verb. I'm also aware that in the field of statistics, one still refers to a datum, a single data point. But this isn't statistics, and I'm not writing in Latin. In English, we have a long history of adopting plural Latin nouns as corporate nouns, and in American English, those nouns take a singular verb. It's what we do when we speak, and it's what I've done in the text. We say the data is reliable not the data are reliable. (I have actually heard the datums are reliable, but that's just sad.) This usage has been adopted by several influential publications, and I have adopted it here. Not because I don't know how Latin works, but because I've carefully considered the issue and decided to write American English as I, as a well-educated native American English speaker, speak it. Now ain't that just about enough on the subject? 0321290933P12232004
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这本书的章节结构安排体现了极高的匠心。它不是按照技术栈的顺序来编排,而是遵循了问题解决的逻辑链条。开篇的“需求捕获与领域建模”部分,我感觉自己像是被拉回了项目最初的起点,作者强调了业务语言的重要性,认为不理解业务,再精妙的数据库设计也只是空中楼阁。紧接着,在谈到数据冗余和一致性时,作者没有急于展示复杂的范式理论,而是从业务数据流动的角度,引导读者去思考“什么是真正的冗余”,以及在何种情况下,适当的冗余是提高查询效率的必要之恶。这种由宏观到微观,再由理论回归实践的叙事方式,让阅读体验非常流畅。我尤其喜欢其中穿插的“设计陷阱回顾”小节,这些都是作者亲身踩过的坑,用幽默且警示性的口吻描述了那些看似微小却能导致系统崩溃的决策失误。这种坦诚的分享,极大地增强了这本书的实战价值,让人觉得它不仅仅是一本教材,更像是一份来自前辈的经验备忘录。
评分坦率地说,这本书的理论深度确实需要读者投入相当的精力去消化。特别是涉及底层存储原理的那几章,关于B+树变体、页分裂和WAL(Write-Ahead Logging)的深入剖析,已经触及了系统编程的范畴。如果一个读者只是抱着“学会写SQL”的目的来翻阅,可能会感到有些吃力。然而,对于那些致力于成为资深DBA或数据库内核工程师的人来说,这简直是如饥似渴的知识盛宴。作者对并发控制机制的讲解极其细致,他不仅解释了MVCC(多版本并发控制)的工作原理,还详细对比了不同数据库如何实现快照隔离,以及这些实现方式如何影响应用层面的死锁行为。书中还包含了一个非常详细的章节,专门讨论了如何针对OLAP和OLTP工作负载设计不同的数据组织结构,比如列式存储和行式存储的选择标准,配有详尽的性能对比图表。我感觉这本书的价值在于,它强迫你去思考“为什么”而不是仅仅“怎么做”,这种对底层机制的透彻理解,才是构建高可靠、高性能系统的基石。
评分这本书的封面设计得相当吸引人,那种深邃的蓝色调,配上简约的几何图形,立刻给人一种专业、严谨的感觉。我尤其欣赏作者在引言部分对“有效性”的深入探讨,远超出了教科书式的定义。他并非仅仅停留在理论层面,而是将数据库设计的哲学融入到每一个技术决策的讨论中。例如,在数据建模章节,作者引入了一种非常直观的类比方法,将复杂的实体关系映射到日常生活中具体的业务场景,这对于初学者来说无疑是一座坚实的桥梁。我记得他详细阐述了在面对高度耦合的遗留系统时,如何运用“解耦优先”的原则进行重构,并辅以几个不同行业(金融、物流)的案例分析。这些案例不仅展示了理论如何落地,更重要的是,它们揭示了在真实世界中,规范和灵活性之间那种微妙的权衡艺术。读完这部分,我感觉自己对“好的设计”的理解得到了质的提升,它不再是抽象的完美蓝图,而是基于现实约束的、动态演进的解决方案。作者的语言风格非常沉稳,充满了对技术细节的敬畏,但绝不晦涩难懂,仿佛一位经验丰富的架构师在耳边娓娓道来他的心路历程。
评分从排版和配图的角度来看,这本书的质量也属上乘。图文并茂的设计使得复杂的概念得以清晰展现。例如,在描述数据分区(Partitioning)策略时,作者使用的动态示意图(虽然是静态的插图,但视觉效果极佳)清晰地展示了哈希分区、范围分区和列表分区在数据均匀分布上的差异,比纯文字描述直观太多了。此外,附带的在线资源链接和代码片段也维护得很好,我下载了其中一个关于DDL变更风险评估的Python脚本,发现它能很好地模拟在线表结构修改可能带来的锁定时间。这本书的语言风格非常克制和精确,几乎没有华丽的辞藻堆砌,每一句话似乎都经过了反复的斟酌,确保信息的密度和准确性。读完后,我合上书本,脑海中浮现的不是一堆零散的知识点,而是一个清晰的、层层递进的数据库设计方法论框架,它指导着我如何从零开始构建一个面向未来的数据平台。这本书无疑是一部久经考验的、面向专业人士的经典之作。
评分我不得不说,这本书在处理性能优化和扩展性问题时,展现出了非凡的深度和广度。许多同类书籍往往只是泛泛而谈索引优化或者缓存策略,但这本书却深入剖析了不同存储引擎(如InnoDB与RocksDB)在特定工作负载下的底层差异。作者花了大量的篇幅来解释查询计划的生成过程,并提供了一套系统性的“性能诊断流程图”,这个流程图简直是救星!我尝试用书中的方法去分析我们当前生产环境中的一个高延迟查询,仅仅通过作者指导的几个关键指标的对比分析,就迅速定位到了一个此前被忽略的统计信息过时的问题,解决了困扰团队数周的性能瓶颈。更让我眼前一亮的是,关于分布式事务的讨论。作者没有回避两阶段提交的固有缺陷,而是坦诚地对比了Saga模式、TCC等更现代的解决方案,并给出了在微服务架构下选择何种一致性模型的决策树。这种全面且不偏不倚的分析角度,使得读者能够真正根据自己的业务需求做出最合理的工程选择,而不是盲目追逐最新的技术热词。
评分apparently, when the instructor only talks about the shallowest stuff to get by, you gotta read some deeper words from experts who sincerely want to teach.
评分apparently, when the instructor only talks about the shallowest stuff to get by, you gotta read some deeper words from experts who sincerely want to teach.
评分apparently, when the instructor only talks about the shallowest stuff to get by, you gotta read some deeper words from experts who sincerely want to teach.
评分apparently, when the instructor only talks about the shallowest stuff to get by, you gotta read some deeper words from experts who sincerely want to teach.
评分apparently, when the instructor only talks about the shallowest stuff to get by, you gotta read some deeper words from experts who sincerely want to teach.
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