This one goes out to the sysadmins in the crowd.

oh god i felt this one. Devs too busy, incompetent or just plain lazy to figure out why their code is so slow, so just have ops throw more CPU and memory at it to brute force performance. Then ops gets to try to explain to management why we are spending $500k per month to AWS to support 50 concurrent users.

The sad thing: Throwing hardware at a problem was actually cheaper for a long time. You could buy that $1500 CPU and put it in your dedicated server, or spend 40 developer hours at $100 a pop. Obviously I'm talking about after the easy software side optimizations have already been put in (no amount of hardware will save you if you use the wrong data structures).
Nowadays you pay $500 a month for 4 measly CPU cores in Azure. Or "less than 1 core" for an SQL Server.
Obviously you have a lot more scalability and reliability in the cloud. But for $500 a month each we had a 16 core, 512 GB RAM machine in the datacenter (4 of them). That kind of hardware on AWS or Azure would bankrupt most companies in a year.
Well, having been on the other side, sometimes the Dev is also trying to fight the good fight whilst having to use some crap 3rd party system/library that's imposed from above because somebody at the C-suite level after suitably dinned and wined (and who knows what more, including implied or even explicit promises for the future of their career) signed a massive agreement with one of the big corporate software providers so now those of us at the coalface have to justify to money spent on that contract by using every POS from said big corporate software provider.
I mean, I might be exagerating the overtly corrupt nature of the deal (in my experience its more a mix of CTO incompetence - or being pretty much powerless at the C-Suite level because his is not the core business, hence overriden - and the high-level management trading favours using company money and more for personal rather than corporate reasons) but even competent devs that know their thing can't really do much when they have to use a bug-riddled POS massive framework from some vendor that doesn't even have proper support, for "corporate reasons".
- I got somebody at the C-suite level fired after I presented evidence of him wining and dining with a shit supplier (actually being buddy buddy and literally dining with him on a weekly basis), also for not knowing the consequences of his decisions and also for him bring unable to keep his hands off employees below him (me included).
  Within 3 months there were 5 severe complaints against him with the CEO and humans resources.
  The company had whistleblower protections but obviously fired me for my troubles as well anyway.
  I don't care, the fucker was evil and the company honestly too and I'm happy I'm gone there.
You and me both

This happens all the time. Companies are bleeding money into the air every second to aws, but they have enough money to not care much.

AWS really was brilliant in how they built a cloud and how they marketed everything as "pay only for what you use".

We worked with a business unit to predict how many people they would migrate on to their new system week 1-2 … they controlled the migration through some complicated salesforce code they had written.
We were told “half a million first week”. We reserved capacity to be ready to handle the onslaught.
8000 appeared week 1.
That this is deemed brilliant is the sad part.
- I mean, I would put brilliant in quotes in the way that it's brilliant for their profits. Not brilliant in the way of making the world a better place.
- Lawful Evil is still evil.
Do you mean that it's still the case that more resources are allocated than actually used or that the code does not need to be optimized anymore due to elastic compute?
- I think both are consequences of the cloud.
  It's cheaper for companies to just add more compute than to pay devs to optimize the code.
  And it's also not so important to overpay for server capacity they don't use.
  Both of these things leads to AWS making more money.
  It's also really good for aws that once these things are built, they just keep bringing in money on their own 24 hours per day.

Meanwhile I'm given a 16gb of ram laptop to compile Gradle projects on.

My swap file is regularly 10+ gigs. Pain.

That reminded me about trying to compile a rust application (Pika Backup) on a laptop with 4 GB of RAM (because AUR).
That was a fun couple of attempts. Eventually I just gave up and installed a flatpak.
- God bless flatpack in times of need

I really hope those aren't factorials.

Depends on which crappy software vendor I'm dealing with in any given week. lol

Why did it change from 64 gb of ram to 1.268869321 E+89(64!) gb of ram

Also, 2.092278988 E+13(16!) gb is a lot more than 64 gb

Bonus if the vendor refuses to provide any further support until your department signs off on the resource expansion.

In a just world that's when you drop the vendor. In a just world.

cough Oracle cough
Then we'd probably have to drop each and every vendor...😩

64! is a whole lot more than 64 though. It's a number with 90 digits.

Hmm is unexpected factorial a sub here yet?
- They're called “communities”, not “subs”.

For some reason I love this meme.

Flip side of the coin, I had a sysadmin who wouldn’t increase the tmp size from 1gb because ‘I don’t need more than that recommended size’. I deploy tons of etl jobs, and they download gbs of files for processing to this globally known temp storage. I got it changed for one server successfully after much back and forth, but the other one I just overrode it in my config files for every script.

This is why Java rocks with ETL, the language is built to access files via input/output streams.
It means you don't need to download a local copy of a file, you can drop it into a data lake (S3, HDFS, etc..) and pass around a URI reference.
Considering the size of Large Language Models I really am surprised at how poor streaming is handled within Python.
- Yeah python does lack in such things. Half a decade ago, I setup an ml model for tableau using python, and things were fine until one day it just wouldn’t finish anymore. Turns out the model got bigger and python filled out the ram and the swap trying to load the whole model in memory.

Yeah, almost certainly the software only uses 4GB because it limits itself to what memory it has available.

I have seen this conversation pan out a few times already. It has always been because of that, and once expanded things work much better. (Personally I have never took party at one, I guess that's luck.)

I phrased that panel poorly. App in question always had 16 GB and still never used above 4.
Does the OS really eat up 7GB RAM?
- Oh, ok, I overlooked it has space to grow. This is one conversation I've never seen.

Great. Now my left eye is twitching uncontrollably and I want to punch a sales drone into next quarter.

narrows eyes

Look I don't "think" that was me this last few weeks. I'm pretty sure my support engineer butt was smart enough to check resources before blaming RAM...

But it totally could have been me, and in that case I blame dev.

The second guy is the exact same type of person as my Dad.

snif

I feel so seen

I loath memory reservation based scheduling. it's always a lie, always. Looking at you, Hadoop.

https://youtu.be/pFlcqWQVVuU?si=E7XxLW64539jGqL3

Here is an alternative Piped link(s): https://piped.video/pFlcqWQVVuU?si=E7XxLW64539jGqL3
Piped is a privacy-respecting open-source alternative frontend to YouTube.
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What kind of apps are we talking about here?

Major ones.