On the other hand, there is a real crossroad that pops up that HNers tend to dismiss.
A common story is that since day one you just have lightweight app servers handling http requests doing 99% I/O. And your app servers can be deployed on a cheap box anywhere since they're just doing I/O. Maybe they're on Google Cloud Run or a small cluster of $5 VPS. You've built them so that they have zero deps on the machine they're running on.
But then one day you need to do some sort of computations.
One incremental option is to create a worker that can sit on a machine that can crunch the tasks and a pipeline to feed it. This can be seen as operationally complex compared to one machine, but it's also simple in other ways.
Another option is to do everything on one beefy server where your app servers just shell out the work on the same machine. This can be operationally simple in some ways, but not necessarily in all ways.
In 2010 I was managing 100 servers, with many Oracle and Postgres DB, PHP, Apache, all on Solaris and Sun HW. I was constantly impressed by how people were unable to do more or less correct estimations. I had a discussion with my boss, he wanted to buy 8 servers, I argued one was more than enough. The system, after growing massively, was still in 2020 managing the load with just 3 servers. So I would argue, not only today, but 15 years ago already.
If you know anything about hardware and look at the typical instances AWS is serving up (other than the ludicrously expensive ones) it's Skylake and older.
I think people have a warped perception of performance, if only because the cloud providers are serving up a shared VM on equipment I'd practically class as vintage computing. You could throw some of the same parts together from eBay and buy the whole system with less than a few months worth of the hourly on-demand cost.
On the other hand, there is a real crossroad that pops up that HNers tend to dismiss.
A common story is that since day one you just have lightweight app servers handling http requests doing 99% I/O. And your app servers can be deployed on a cheap box anywhere since they're just doing I/O. Maybe they're on Google Cloud Run or a small cluster of $5 VPS. You've built them so that they have zero deps on the machine they're running on.
But then one day you need to do some sort of computations.
One incremental option is to create a worker that can sit on a machine that can crunch the tasks and a pipeline to feed it. This can be seen as operationally complex compared to one machine, but it's also simple in other ways.
Another option is to do everything on one beefy server where your app servers just shell out the work on the same machine. This can be operationally simple in some ways, but not necessarily in all ways.
In 2010 I was managing 100 servers, with many Oracle and Postgres DB, PHP, Apache, all on Solaris and Sun HW. I was constantly impressed by how people were unable to do more or less correct estimations. I had a discussion with my boss, he wanted to buy 8 servers, I argued one was more than enough. The system, after growing massively, was still in 2020 managing the load with just 3 servers. So I would argue, not only today, but 15 years ago already.
Most younger devs just have no concept on how limited hardware we ran services on...
I used to run a webmail system with 2m accounts on hardware with less total capacity (ram, disk, CPU throughput) than my laptop...
What's more: It was a CGI (so new process for every request), and the storage backend spawned separate processes per user.
If you know anything about hardware and look at the typical instances AWS is serving up (other than the ludicrously expensive ones) it's Skylake and older.
I think people have a warped perception of performance, if only because the cloud providers are serving up a shared VM on equipment I'd practically class as vintage computing. You could throw some of the same parts together from eBay and buy the whole system with less than a few months worth of the hourly on-demand cost.
Indeed - they are incredibly fast, it's just buried under layers upon layers of stuff
No worries, another fifteen layers of software abstraction will soak that up pronto.