One of the patterns I picked up during my freelancing years was to gently probe any time a client made an absolute statement. “We always do X” or “Y never happens.” These were usually non-technical clients describing their business processes, and I needed to understand them well enough to build software around them.
Honestly, I can’t remember a single one of those statements holding up to any scrutiny. Most fell apart with a single question. “What if Z happens?” “Oh, in that case, we do this other thing.” Almost none of them survived two or three follow-ups. It wasn’t that people were lying or even wrong, they just had a mental model of how things worked that was cleaner than reality.
This matters a lot when you’re building abstractions. When you create a shared component or a unified data model, you’re betting that these things really are the same. That they’ll change together. That the commonality you see today will hold tomorrow. Sandi Metz said “Duplication is far cheaper than the wrong abstraction.” I’d take it further, you need to be really certain an abstraction is right, or it’s probably wrong.
Abstractions and DRY are, in a sense, intentional single points of failure. That’s not to say they’re bad (I build them all the time), but it’s worth keeping in mind. You are hitching a lot to the same post. If you try to abstract physical addresses to cover all addresses everywhere, you’re left with basically zero rules because they’re all broken somewhere. Same for names, medical histories, pretty much any taxonomy that involves humans.
So now when I’m looking at a potential abstraction, I try to pressure-test it. “Users always have an email address” is fragile, there’s probably an exception lurking somewhere. “Users usually have an email address, and we handle it gracefully when they don’t” is something you can build on. If your abstraction can’t survive that kind of flex, it might not be an abstraction at all, just a few things that happen to look similar, until they don’t.