I am extremely Not A Database Person but I understand that the rationale for Kubernetes adopting etcd as its preferred data store was more about its distributed consistency features and less about query throughput. etcd is slower cause it's doing RAFT things and flushing stuff to disk.

Projects like kine allow K8s users to swap sqlite or postgres in place of etcd which (I assume, please correct me otherwise) would deliver better throughput since those backends don't need to perform consenus operations.

https://github.com/k3s-io/kine

it's not really bottlenecked by the store but by the calculations performed on each pod schedule/creation.

It's basically "take global state of node load and capacity, pick where to schedule it", and I'd imagine probably not running in parallel coz that would be far harder to manage.

It says in the blog that they require 13,000 queries per second to update lease objects, not that 13,000 is the total for all queries. I don't know why they cite that instead of total, but etcd's normal performance testing indicates it can handle at least 50,000 writes per second and 180,000 reads: https://etcd.io/docs/v3.6/op-guide/performance/. So, without them saying what the real number is, I'm going to guess their reads and writes outside of lease updates are at least much larger than those numbers.

> I see the appeal of K8s in dividing raw, stateful hardware to run multiple parallel workloads, but if you're dealing with stateless cloud VMs, why would you need K8S and its overhead when the VM hypervisor already gives you all that functionality?

I think you're not familiar with Kubernetes and what features it provides.

For example, kubernetes supports blue-green deployments and rollbacks, software-defined networks, DNS, node-specific purges and taints, etc. Those are not hypervisor features.

Also, VMs are the primitives of some cloud providers.

It sounds like you heard about how Borg/Kubernetes was used to simplify the task of putting together clusters with COTS hardware and you didn't bothered to learn more about Kubernetes.

Because k8s gives you lots of other things out of the box like easy scaling of apps etc. Harder to do on VM:s where you would either have to dedicate one VM per app (might be a waste of resources) or you have to try and deploy and run multiple apps on multiple VM:s etc.

(For the record I’m not a k8s fanatic. Most of the time a regular VM is better. But a VM isn’t = a kubernetes cluster).

In a large organization their more efficient to run on VMS. You can colocate services that fit together on one machine.

And in reality no one sizes their machines correctly. They always do some handwavey thing like we need 4 cores, but maybe well burst and maybe there will be an outage so lets double it. Now all that utilization can be watched and you can take advantage of over subscription.

because if you just do a few huge VMs you still have all the problems that k8s solves out of the box. Except now you have to solve them yourself, which will likely end up being a crappier less robust version of kubernetes.

The reason to target k8s on cloud vms is that cloud VMs don't subdivide as easily or as cleanly. Managing them is a pain. K8s is an abstraction layer for that - Rather than building whole machine images for each product, you create lighter weight docker images (how light weight is a point of some contention), and you only have to install your logging, monitoring, and etc once.

Your advice about bigger machines is spot on - K8s biggest problem is how relatively heavyweight the kublet is, with memory requirements of roughly half a gig. On a modern 128g server node that's a reasonable overhead, for small companies running a few workloads on 16g nodes it's a cost of doing business, but if you're running 8 or 4g nodes, it looks pretty grim for your utilization.

That's simulated using kwok, not real.

> Unfortunately running 1M real kubelets is beyond my budget.

THis is a PoC not backed by a reliable etcd replacement.

That is 100k vs 130k for Google’s new announcement. I can’t speak as to whether the additional 30k presented new challenges though.

fuse based filesystems in general shouldn’t be treated as production ready in my experience.

They’re wonderful for low volume, low performance and low reliability operations. (browsing, copying, integrating with legacy systems that do not permit native access), but beyond that they consume huge resources and do odd things when the backend is not in its most ideal state.

Same here. Non Kubernetes project originated control plane components start failing beyond a certain limit - your ingress controllers, service meshes etc. So I don't usually take node numbers from these benchmarks seriously for our kind of workloads. We run a bunch of sub-1k node clusters.

Same. The control plane and various controllers just aren't up to the task.

Meh, I've had had clusters with close to 1k nodes (w/ cilium as CNI) and didnt have major issues

The could team up with Microsoft, because their signup flow is fine but the login flow is badly broken.

For what? Access to the control plane API?

> You are not Google.

100% agree.

People at my co are horny to adopt k8s. Really, tech leads want to put it on their resume ("resume driven development") and use a tool that was made to solve a particular problem we never had. The downside is now we now need to be proficient it at, know how to troubleshoot it, etc. It was sold to leadership as something that would make our lives easier but the exact opposite has happened.

I work for a mature public company that most people in the US have at least heard of. We're far from the largest in our industry and we run jobs with more than that almost every night. Not via k8s though.

>You are not Google.

It's literally Google coming out with this capability and how is the criticism still "You are not Google"