Reddit mentions of Transaction Processing: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

I'm assuming you are interested in designing schemas for relational databases, and not writing relational databases.

I really like CJ Date's SQL and Relational Theory and Database Design and Relational Theory. Use The Index, Luke is a nice site, too, with generally less formal information than Date's books, but more practical info about day-to-day problems.

If you'd like to dig under the covers, I'd start with Principles of Transaction Processing. Philip Bernstein knows what he is talking about, and is active in database research. Gray and Reuter's Transaction Processing is a classic, but is less approachable.

You should first read "Transaction Processing, Concepts and Techniques" http://www.amazon.com/Transaction-Processing-Concepts-Techniques-Management/dp/1558601902/

There's also Michael Stonebraker's "Red Book"

After that, a lot of resources available on the Web... and source code of course.

> For those who prefer video lectures, Skiena generously provides his online. We also really like Tim Roughgarden’s course, available from Stanford’s MOOC platform Lagunita, or on Coursera. Whether you prefer Skiena’s or Roughgarden’s lecture style will be a matter of personal preference.
>
> For practice, our preferred approach is for students to solve problems on Leetcode. These tend to be interesting problems with decent accompanying solutions and discussions. They also help you test progress against questions that are commonly used in technical interviews at the more competitive software companies. We suggest solving around 100 random leetcode problems as part of your studies.
>
> Finally, we strongly recommend How to Solve It as an excellent and unique guide to general problem solving; it’s as applicable to computer science as it is to mathematics.
>
>
>
> [The Algorithm Design Manual](https://teachyourselfcs.com//skiena.jpg) [How to Solve It](https://teachyourselfcs.com//polya.jpg)> I have only one method that I recommend extensively—it’s called think before you write.
>
> — Richard Hamming
>
>
>
> ### Mathematics for Computer Science
>
> In some ways, computer science is an overgrown branch of applied mathematics. While many software engineers try—and to varying degrees succeed—at ignoring this, we encourage you to embrace it with direct study. Doing so successfully will give you an enormous competitive advantage over those who don’t.
>
> The most relevant area of math for CS is broadly called “discrete mathematics”, where “discrete” is the opposite of “continuous” and is loosely a collection of interesting applied math topics outside of calculus. Given the vague definition, it’s not meaningful to try to cover the entire breadth of “discrete mathematics”. A more realistic goal is to build a working understanding of logic, combinatorics and probability, set theory, graph theory, and a little of the number theory informing cryptography. Linear algebra is an additional worthwhile area of study, given its importance in computer graphics and machine learning.
>
> Our suggested starting point for discrete mathematics is the set of lecture notes by László Lovász. Professor Lovász did a good job of making the content approachable and intuitive, so this serves as a better starting point than more formal texts.
>
> For a more advanced treatment, we suggest Mathematics for Computer Science, the book-length lecture notes for the MIT course of the same name. That course’s video lectures are also freely available, and are our recommended video lectures for discrete math.
>
> For linear algebra, we suggest starting with the Essence of linear algebra video series, followed by Gilbert Strang’s book and video lectures.
>
>
>
> > If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is.
>
> — John von Neumann
>
>
>
> ### Operating Systems
>
> Operating System Concepts (the “Dinosaur book”) and Modern Operating Systems are the “classic” books on operating systems. Both have attracted criticism for their writing styles, and for being the 1000-page-long type of textbook that gets bits bolted onto it every few years to encourage purchasing of the “latest edition”.
>
> Operating Systems: Three Easy Pieces is a good alternative that’s freely available online. We particularly like the structure of the book and feel that the exercises are well worth doing.
>
> After OSTEP, we encourage you to explore the design decisions of specific operating systems, through “{OS name} Internals” style books such as Lion's commentary on Unix, The Design and Implementation of the FreeBSD Operating System, and Mac OS X Internals.
>
> A great way to consolidate your understanding of operating systems is to read the code of a small kernel and add features. A great choice is xv6, a port of Unix V6 to ANSI C and x86 maintained for a course at MIT. OSTEP has an appendix of potential xv6 labs full of great ideas for potential projects.
>
>
>
> [Operating Systems: Three Easy Pieces](https://teachyourselfcs.com//ostep.jpeg)
>
>
>
> ### Computer Networking
>
> Given that so much of software engineering is on web servers and clients, one of the most immediately valuable areas of computer science is computer networking. Our self-taught students who methodically study networking find that they finally understand terms, concepts and protocols they’d been surrounded by for years.
>
> Our favorite book on the topic is Computer Networking: A Top-Down Approach. The small projects and exercises in the book are well worth doing, and we particularly like the “Wireshark labs”, which they have generously provided online.
>
> For those who prefer video lectures, we suggest Stanford’s Introduction to Computer Networking course available on their MOOC platform Lagunita.
>
> The study of networking benefits more from projects than it does from small exercises. Some possible projects are: an HTTP server, a UDP-based chat app, a mini TCP stack, a proxy or load balancer, and a distributed hash table.
>
>
>
> > You can’t gaze in the crystal ball and see the future. What the Internet is going to be in the future is what society makes it.
>
> — Bob Kahn
>
> [Computer Networking: A Top-Down Approach](https://teachyourselfcs.com//top-down.jpg)
>
>
>
> ### Databases
>
> It takes more work to self-learn about database systems than it does with most other topics. It’s a relatively new (i.e. post 1970s) field of study with strong commercial incentives for ideas to stay behind closed doors. Additionally, many potentially excellent textbook authors have preferred to join or start companies instead.
>
> Given the circumstances, we encourage self-learners to generally avoid textbooks and start with the Spring 2015 recording of CS 186, Joe Hellerstein’s databases course at Berkeley, and to progress to reading papers after.
>
> One paper particularly worth mentioning for new students is “Architecture of a Database System”, which uniquely provides a high-level view of how relational database management systems (RDBMS) work. This will serve as a useful skeleton for further study.
>
> Readings in Database Systems, better known as the databases “Red Book”, is a collection of papers compiled and edited by Peter Bailis, Joe Hellerstein and Michael Stonebreaker. For those who have progressed beyond the level of the CS 186 content, the Red Book should be your next stop.
>
> If you insist on using an introductory textbook, we suggest Database Management Systems by Ramakrishnan and Gehrke. For more advanced students, Jim Gray’s classic Transaction Processing: Concepts and Techniques is worthwhile, but we don’t encourage using this as a first resource.
>

> (continues in next comment)

Transaction Processing by Jim Gray

For learning about RDBMS internals (i.e. not how to use one), you should at least add Architecture of a Database System by Stonebraker et al. to your reading list. It'll give you a decent overview.

From there, it depends a little on what you're interested in. Transaction processing? Replication? Query evaluation? Query optimizing? The latter specifically is hard to find much useful on in books --- most useful information is found in scientific papers. (I just wrote a master's thesis about query optimization... :)

If you want something inbetween, i.e. necessary knowledge about internals to use the systems more efficiently, Physical Database Design is worth a read.