But I think a lot of it is also about knowledge and documentation. If I want to copy FAANG or another startup, and set up an infinitely scalable queue-based architecture, I can find dozens of high quality guides, tutorials, white papers etc, showing me exactly how to do it. Yes maintenance is higher, but I can get set up with redis, SQS, any of the 'scalable' solutions within a few hours of copy-pasting commands and code and configuration from a reputable source.
If I want to use NOTIFY in postgres? I googled "SQLALchemy notify listen postgres" and I find a few unanswered stackoverflow questions and a github gist that has some code but no context.
I would honestly love to use this approach for a side project, but I don't have 2-3 days to figure it out on my own. The direct choice for me might seem to be
* simple, but not scalable (ie just use postgres)
* complex, but scalable (ie redis, sqs, whatever)
and then it's a tradeoff, and the argument goes that I am blinded by cool tech and FAANG and I'm choosing complex but scalable, even though I don't need scalable.
But taking into account guides and other resources, the choice for me is actually
* complex and not scalable (this, because I don't know how to implement it and I can't predict what pitfalls I might face if I try)
* simple and scalable (what everyone actually does)
and that makes the engineer's choice to follow faang look a lot more reasonable.
For instance, if you use postgres with a low load, it is almost trivial to migrate schemas, add new constraints, do analytics etc.
If you use SQS, Cassandra, whatever, then you now get scalability/availability but it becomes much more time-consuming to change things if you figure out that your original design doesn't work. Say the business comes and says "please add constraint X. All users of type foo must never combined value bar at the same time."
It is possible to implemented that without postgres, but it is not easy or simple, especially if you need to make changes.
Therefore, my take is that you either use postgres to stay flexible or you use both postgres and something else on top of it when you know that you won't have to change things. Of course this means additional infrastructure/maintenance overhead.
In the end it's always a trade-off, you just need to know when to trade which thing off against what.
There's a (sort of) objective trade-off to be made, but another dimension is how familiar you are with the solution and/or how quick can you implement it using documentation and examples.
If you happen to know exactly how to create a horizontally scalable microservice based hairball with nodejs, then maybe you are quicker with that than with some traditional django monolith using a nicely normalized sql database (or whatever).
In a startup, you are often always squeezed for time, so making the objectively right tradeoff for your context is usually secondary to the simple question of 'when can you ship?' If the scalable-yet-inflexible is what stack overflow abundantly recommends and documents, maybe this is quicker to get done now, whatever the consequences are on the longer run.
If someone says "I choose X over Y because I used X before (or because X is better documented" then fair enough - but I rarely hear that as an argument when choosing "FAANG-technology".
> If I want to copy FAANG or another startup, and set up an infinitely scalable queue-based architecture, I can find dozens of high quality guides, tutorials, white papers etc, showing me exactly how to do it.
I'm not sure about this either, though from reading typical developer blogs and listening to the hivemind, you do get the feeling that you must be scalable. Devs often don't really know when (usually not) that becomes important and how far the vast majority of apps can go with monoliths in big boxes (quite far).
But my response would then be that this is a stupid example in the context of this whole submission because that submission talks about postgres and trying to get postgres to scale "infinitely" let alone fulfill other properties like extremely high uptime etc. that is just... insane. No one in their right mind tries to do that with postgres. It is one thing to do queueing with it but "infinitely scalable" is a totally different one.
Therefore I can only say: yeah, to set up "an infinitely scalable queue-based architecture" you should not use postgres and the author in the submission says the same thing.
> Devs often don't really know when (usually not) that becomes important and how far the vast majority of apps can go with monoliths in big boxes (quite far).
Right, they make the wrong trade-offs. That is exactly what I wanted to express with my response.