Well, the advice is rarely taken in practice. It is (in my experience, and it seems common from others based on what I've heard) very very common to jump to the complicated solution at the first hint of capacity issues "because we'll need to do it eventually anyway."

The advice is obvious when you're thinking at that level of abstraction. Which suggests that, in practice, people who are architecting such systems rarely think at that level of abstraction. Which is why it is nice to have posts like this, that periodically remind us to get our heads out of the daily minutiae and consider the bigger picture (of complexity tradeoffs, realistic projections, staffing and availability, etc.)

It might be obvious as far as it goes, but it's also incomplete in at least two ways. One is that as tweaks and optimizations and "supplementing the system in some way" often involves increasing its complexity, even if just a little bit at a time. It adds up with time. The more important thing is this: if you're already constrained on vertical scaling, and you don't have a firm grip on how fast your system is scaling, then you can't just stop with making the db more efficient. That's just postponing the inevitable, and possibly not for more than a couple of years. If you're in the position the author portrays, get the database under control first -- for sure -- but then get started on figuring out how you're going to stay in front of your scaling problem, whether that's rearchitecture, off-loading work to systems better suited for it, or whatever. Speaking as a former owner of a very large Amazon database that fought this battle many times, trying to buy enough dev time to build away from it before it completely collapsed. We were too content with performance improvements just like the ones described in this article, before finally recognizing we were just racing the clock.

That no one likes to follow.

“Common sense is not so common.”

- Voltaire