The reason I ask is I'm working on a product with 30-ish different pods/teams (maybe about 200 - 250 engineers) working on their respective modules, microservices, etc. From my understanding with talking to a lot of others at conferences, is that our distributed system is fairly small (in terms of functional modules/teams, transactions, etc..).
Anyway, even at our small-ish scale, I couldn't imaging running our platform as a single app that were were able to scale up with better hardware.
Also, I think how a company supports multi-tenanting would play a big role in deciding how this works, too, because you can have scenario with a monolith and DB but you have it partitioned by individual tenant dbs, app servers, etc, and you still have a huge pile of hardware (real or virtual) you're dancing around in....
Lots of 250+ engineer teams out there working on monoliths.
AFAIK, writing "Used a large machine to solve customer problems quickly and efficiently" is not really taken well by a lot of people. The majority of companies can better scale up, than out, but out is the new normal for various reasons.
https://stackexchange.com/performance
Where do you get your numbers from?
When the latter was possible it saved time but it was always breaking under me and I eventually said fuck it
Not at all. I spent quite a lot of effort on debugging distributed deadlocks in a highly available system that was the best selling product in its category at one of the most famous (and loved) software companies based in SV and the amount of things that could (and will) go wrong is infinite, given every piece of infrastructure has its difficult-to-find/reproduce bugs. Things like sockets stopping responding after a few weeks due to an OS bug, messing up your distributed log, or unexpected sequences of waking up during a node crash and fail-over because some part of the network had its issues, leading to split brain for a few moments that needs to be resolved real-time, a brief out of memory issue that leads to a single packet loss, messing up your protocol etc. We used sophisticated distributed tests and that was absolutely inadequate. You are still in a naive and optimistic mode, though perhaps you as a designer will be shielded from the real-life issues your poor developers would experience as usually original architects move on towards newer cooler things and leave the fallout to somebody else.
In our case, we use a single process to run everything, in conjunction with strong invariants written into the code. When an invariant is violated, we analyze the log to figure out the sequence of events that triggers the violation. As the execution is multi-threaded, the analysis can take a while. If the execution was single-threaded, the analysis would be much easier. In practice, the analysis is usually quick because the log tells us what is going on in each thread.
So, I guess there is a trade-off between the overhead of building the test framework and the extra effort in log analysis.
We have gone through the pain of testing and debugging a distributed system that is under development for the past 5 years. We investigated several fancy ways of debugging distributed systems such as model checking and formal verification. In the end, we decided to use (and are more or less happy with) the traditional way. The decision was made mostly because 1) the implementation is too complex (a lot of Java and Scala code) to allow formal techniques; 2) the traditional way can still be very effective when combined with careful design and thorough testing.
Before building the distributed system, we worked on formal verification using program logic and were knowledgeable about a particular sub-field. From our experience, I guess it would require a PhD dissertation to successfully apply formal techniques to debugging our distributed system. The summary of our experience is at https://www.datamonad.com/post/2020-02-19-testing-mr3/.