This always sounds good on paper and this is very common lore, but then when you get into production escalations a very common problem is a lot of software depends on limits for autoconfiguration of thread pools and even several runtimes (go and java for example, at least .net is mentioned in the article), yes you can usually set them with a flag but people have to know this, communicate it, enforce it. Basically replace adhoc what limits is doing for you automatically configuration wise
So this just all assumes you have a setup where all teams communicate the necessary information perfectly.. what happens in practice is workloads degrade at edge cases because there are 256 threads running for a thread pool instead of 4.
Go is particularly hilarious because it will just go and create a thread for every hardware thread in your system (my home system has 192 hardware threads), just in case it needs to scale up. So tiny utilities, little network proxies and the like add up to thousands of threads + stacks etc. for very little load.
While I agree that CPU limits tend to make your performance worse, I don't think the delivery of the post is all too convincing (and is pretty heavy on the LLM-isms that it's putting me off from reading).
It mentions that a cpu request is a guarantee, but how is that enforced? If I have 32 pods running on a 32 core machine, each with 1cpu requested, what stops one of those pods using an unfair share? I assume we just rely on the Linux scheduler. If I have 16 pods with 1cpu and 1 pod with 16cpu, does the Linux scheduler make sure to give the 16cpu pod more time? Or are we back to using cgroups.
> If I have 32 pods running on a 32 core machine, each with 1cpu requested, what stops one of those pods using an unfair share?
I don't use all this fancy stuff, but wouldn't you set that up as each of the 32 pods is limited to running on a specific core? No sense letting them each run on all cores, because all of the X per core will get too big.
I would expect the impact of cpu limits to be different between K8s providers. I have only used memory limits. If I had a pod that tended to be very cpu intensive, I would schedule it on it's own node group.
It's not as much an "overhead" as it will mess up your latency. Limits don't stop you from using available resources until you hit the relative allowance in a CFS window, so a 1 CPU limit on a 32 CPU machine at worst gives you 32 cores for 3.3ms every 100ms.
Could it be that they're using coding agents to develop applications they previously wouldn't spent time on developing? Like internal tools, or experimental builds.
Some workloads will also consume all the resources you hand them without being latency sensitive at all.
I've handled outages of CPU time available getting suddenly compressed (we were running pod priorities with staging/prod on one cluster and up to 70% spots in 2019) and then learning that some very important applications outgrew their original requests, gone unnoticed because limits were removed a year or so prior. You can fix this with monitoring/right-sizing tools, but that requires your org to not be dysfunctional, and my style of platform engineering usually has to account for the org being very dysfunctional.
For the love of god - care about other pods on the node, especially in a multi-tenant setup.
Sorry for the cheeky response.
CPU Limits have a place, you don't want a bad change for 1 deployment object affect all neighbors by taking all the CPU. You need to be able to constrain the blast radius. This doc gives me strong AI vibes. Setting CPU limits isn't free. You still need to care about how the programming language that you use discovers those limits, and correctly handles them. For e.g. if you spin up a 100 Java threads, but only have 1 cpu as the limit, that's bad design.
I don’t think you understand how CPU limits and the Linux CPU scheduler work. CPU limits don’t protect you from something taking all the CPU; that’s what CPU requests do. Limits throttle your pods even if the CPU is idle/free to do work.
> The CPU request typically defines a weighting. If several different containers (cgroups) want to run on a contended system, workloads with larger CPU requests are allocated more CPU time than workloads with small requests.
So I guess limits _would_ protect other pods to a degree. Though I agree that it doesn't seem worth the tradeoff of your pod getting constantly interrupted while the rest of the box is sitting idle
Where do you think the contradiction is? A CPU limit of 1 does not prevent an application from using 8 cores worth of compute at once, it just limits the compute to 1 core per 100ms slice on average, and usually that means getting unscheduled for 7/8 of the slice, if the app is using all available resources and nothing else contests them (weighted by requests).
So this just all assumes you have a setup where all teams communicate the necessary information perfectly.. what happens in practice is workloads degrade at edge cases because there are 256 threads running for a thread pool instead of 4.
It mentions that a cpu request is a guarantee, but how is that enforced? If I have 32 pods running on a 32 core machine, each with 1cpu requested, what stops one of those pods using an unfair share? I assume we just rely on the Linux scheduler. If I have 16 pods with 1cpu and 1 pod with 16cpu, does the Linux scheduler make sure to give the 16cpu pod more time? Or are we back to using cgroups.
> If I have 16 pods with 1cpu and 1 pod with 16cpu, does the Linux scheduler make sure to give the 16cpu pod more time?
Yes if there's CPU pressure the 16cpu will get 16x more than 1cpu.
I don't use all this fancy stuff, but wouldn't you set that up as each of the 32 pods is limited to running on a specific core? No sense letting them each run on all cores, because all of the X per core will get too big.
There's a part about it: https://github.com/inevolin/k8s-cpu-limits-analyzed#3-withou...
The article behind it explains it better, but a quick glance at the image above is all you need if you don't care too much about the explanation.
https://home.robusta.dev/blog/stop-using-cpu-limits
It was already the case in 2018.
Also, no mention of the scheduler overhead. And the maintenance overhead is the worst.
I’m not sure why that is but a large number of the F100s I contract with are suddenly deploying 4 times the number of containers they had before.
I've handled outages of CPU time available getting suddenly compressed (we were running pod priorities with staging/prod on one cluster and up to 70% spots in 2019) and then learning that some very important applications outgrew their original requests, gone unnoticed because limits were removed a year or so prior. You can fix this with monitoring/right-sizing tools, but that requires your org to not be dysfunctional, and my style of platform engineering usually has to account for the org being very dysfunctional.
(The title of this was also stolen for this HN post, although the GitHub repo makes no mention of it...)
Sorry for the cheeky response.
CPU Limits have a place, you don't want a bad change for 1 deployment object affect all neighbors by taking all the CPU. You need to be able to constrain the blast radius. This doc gives me strong AI vibes. Setting CPU limits isn't free. You still need to care about how the programming language that you use discovers those limits, and correctly handles them. For e.g. if you spin up a 100 Java threads, but only have 1 cpu as the limit, that's bad design.
> The CPU request typically defines a weighting. If several different containers (cgroups) want to run on a contended system, workloads with larger CPU requests are allocated more CPU time than workloads with small requests.
So I guess limits _would_ protect other pods to a degree. Though I agree that it doesn't seem worth the tradeoff of your pod getting constantly interrupted while the rest of the box is sitting idle
https://kubernetes.io/docs/concepts/configuration/manage-res...