[roncesvalles]

That's the most nonsensical reason to adopt microservices imo.

Consider this: every API call (or function call) in your application has different scaling requirements. Every LOC in your application has different scaling requirements. What difference does it make whether you scale it all "together" as a monolith or separately? One step further, I'd argue it's better to scale everything together because the total breathing room available to any one function experiencing unusual load is higher than if you deployed everything separately. Not to mention intra- and inter-process comm being much cheaper than network calls.

The "correct" reasons for going microservices are exclusively human -- walling off too much complexity for one person or one team to grapple with. Some hypothetical big brain alien species would draw the line between microservices at completely different levels of complexity.

[venturecruelty]

At this point, I'm convinced that too many people simply haven't built software in a way that isn't super Kubernetes-ified, so they don't know that it's possible. This is the field where developers think 32 GB of RAM isn't enough in their laptop, when we went to the moon with like... 4K. There is no historical or cultural memory in software anymore, so people graduate not understanding that you can actually handle 10,000 connections per second now on a five-year-old server.

[Nextgrid]

Many developers started their career during the ZIRP era where none of the typical constraints of "engineering" (cost control, etc) actually applied and complexity & high cloud bills were seen as a good thing, so no wonder.

[rockemsockem]

Splitting up responsibilities into separate services should in fact primarily be done to reduce costs.

[lijok]

You’re focusing on the theoretical and ignoring cost. That’s incompetent engineering.

[rockemsockem]

There is in fact software that consumes large amounts of resources for fundamental reasons that saves real dollars when it is split into different units for the purpose of scaling those units independently. Most of that software is not primarily dealing with the number of connections it has.

[rockemsockem]

What are you talking about.

When people talk about scaling requirements they are not referring to minutiae like "this function needs X CPU per request and this other function needs Y CPU per request", they are talking about whether particular endpoints are primarily constrained by different things (i.e. CPU vs memory vs disk). This is important because if I need to scale up machines for one endpoint that requires X CPU but the same service has another endpoint requiring Y memory whereas my original service only needs Z memory and Y is significantly larger than Z then suddenly you have to pay a bunch of extra money to scale up your CPU-bound endpoint because you are co-hosting it with a memory-bound endpoint.

If all your endpoints just do some different logic and all hit a few redis endpoints, run a few postgres queries, and assemble results then keep them all together!!!

EDIT: my original post even included the phrase "significantly different" to describe when you should split a service!!! It's like you decided to have an argument with someone else you've met in your life, but directed it at me

[roncesvalles]

I think I get your point: e.g. some part of your application absolutely needs the world's fastest NVMe as its local disk. Another part of your application just needs more cores from horizontal scaling. If you horiz scale to get more cores, you're also paying to put the world's fastest NVMe on each new machine.

Eh... I think you can achieve this in a simpler way with asymmetric scaling groups and smartly routing workloads to the right group. I feel like it's an ops/infra problem. There's no reason to constrain the underlying infra to be homogenous.