When I was really young and first started coding, I hated relational databases.

They didn’t teach us much about them in university, but they were entirely dominant in enterprise development for the 80s and 90s. If you needed persistence, only an RDBMS could be considered. Any other choice, like rolling it yourself, files, or lighter dbs, caches, was considered inappropriate. People would get mad if you used them.

My first experiences with an RDBMS were somewhat painful. The notion of declarative programming felt a bit foreign to me, and those earlier databases were cruder in their syntax.

But eventually I figured them out, and even came to appreciate all of the primary and secondary capabilities. You don’t just get reliable queries; you can use them for dynamic behaviour and distributed locking issues as well. Optimizations can be a little tiring, and you have to stay tight to normal forms to avoid piling up severe technical debt, but with practice, they are a good, strong, solid foundation. You just have to use them correctly to get the most out of them.

If you need reliable persistence (and you do) and the computations aren’t exocitc (and they mostly aren’t), then relying on a good RDBMS is generally the best choice. You just have to learn a lot about the technology and use it properly, but then it works as expected.

If you try to use it incorrectly, you are doing what I like to call “going against the grain”. It’s an ancient woodworking expression, but it is highly fitting for technology. With the grain, you are using it as the originators intended, and against the grain, you are trying to get it to do something clever, awkward, or funny.

Sometimes people think they are clever by trying to force technology to do something unexpected. But that is always a recipe for failure. Even if you could get it to work with minimal side effects, the technology evolves, so the tricks will turn ugly.

Once you’ve matured as a programmer, you realize that clever is just asking for trouble, and usually for no good reason. Most code, most of the time, is pretty basic. At least 90% of it. Usually, it only needs to do the same things that people have been doing for at least half a century. The problem isn’t coming up with some crazy, clever new approach, but rather finding a very reasonable one in an overly short period of time, in a way that you can keep moving it forward over a long series of upgrades.

We build things, but they are not art; they are industrial-strength machinery. They need to be clean, strong, and withstand the ravages of the future. That is quality code; anything else is just a distraction.

Now, if you are pushing the state of the art for some reason, then you would have to go outside of the standard components and their usages. So, I wasn’t surprised that NoSQL came into existence, and I have had a few occasions where I both needed it and really appreciated it. ORMS are similar.

It’s just that I would not have been able to leverage these technologies properly if I didn’t already understand how to get the most out of an RDBMS. I needed to hit the limits first to gain an understanding.

So, when I saw a lot of people using NoSQL to skip learning about RDBMSes, I knew right away that it was a tragic mistake. They failed to understand that their usage was rather stock and just wanted to add cool new technologies to their resumes. That is the absolute worst reason to use any technology, ever. Or as I like to say, experiment on your own time, take your day job seriously.

In that sense, using an RDBMS for something weird is going against the grain, but skipping it for some other eclectic technology is also going against the grain. Two variations on the same problem. If you need to build something that is reliable, then you have to learn what reliable means and use that to make stronger decisions about which components to pile into the foundations. Maybe the best choice is old, and not great for your resume, but that is fine. Doing a good job is always more important.

This applies, of course, to all technologies, not just RDBMSes. My first instinct is to minimize using any external components, but if I have to, then I am looking for the good, reliable, industrial-strength options. Some super-cool, trendy, new component automatically makes me suspicious. Old, crusty, battle-scarred stuff may not look as sweet, but in most cases, it is usually a lot more reliable. And the main quality that I am looking for is reliability.

But even after you decide on the tech, you still have to find the grain and go with it. You pick some reasonable library, but then try to make it jump around in unreasonable ways; it will not end well. In the worst case, you incorrectly convince yourself that it is doing something you need, but it isn’t. Swamping out a big component at the last minute before a release is always a huge failure and tends to result in really painful circumstances. A hole that big could take years to recover from.

So, it plays back to the minimization. If we have to use a component, then we have to know how to use it properly, so it isn’t that much of a time saving, unless it is doing something sophisticated enough that learning all of that from scratch is way out of the time budget. If you just toss in components for a tiny fraction of their functionality, the code degenerates into a huge chaotic mess. You lose that connection to knowing what it will really do, and that is always fatal. Mystery code is not something you ever want to support; it will just burn time, and time is always in short supply.

In general, if you have to add a component, then you want to try to use all of its core features in a way that the authors expected you to use them. And you never want to have two contradictory components in the same system; that is really bad. Use it fully, use it properly, and get the most out of the effort it took to integrate it fully. That will keep things sane. Overall, beware of any components you rely on; they will not save you time; they may just minimize some of the learning you should have done, but they are never free.