Building the largest known Kubernetes cluster, with 130k nodes
46 comments
·November 21, 2025Nextgrid
locknitpicker
> 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.
victorbjorklund
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).
GauntletWizard
[delayed]
hazz99
I’m sure this work is very impressive, but these QPS numbers don’t seem particularly high to me, at least compared to existing horizontally scalable service patterns. Why is it hard for the kube control plane to hit these numbers?
For instance, postgres can hit this sort of QPS easily, afaik. It’s not distributed, but I’m sure Vitess could do something similar. The query patterns don’t seem particularly complex either.
Not trying to be reductive - I’m sure there’s some complexity here I’m missing!
phrotoma
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.
dijit
You might not be a database person, but you’re spot on.
A well managed HA postgresql (active/passive) is going to run circles around etcd for kube controlplane operations.
The caveat here is increased risk of downtime, and a much higher management overhead, which is why its not the default.
Sayrus
GKE uses Spanner as an etcd replacement.
ZeroCool2u
But, and I'm honestly asking, you as a GKE user don't have to manage that spanner instance, right? So, you should in theory be able to just throw higher loads at it and spanner should be autoscaling?
nonameiguess
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.
PunchyHamster
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.
senorrib
No a k8s dev, but I feel like this is the answer. K8s isn't usually just scheduling pods round robin or at random. There's a lot of state to evaluate, and the problem of scheduling pods becomes an NP-hard problem similar to bin packing problem. I doubt the implementation tries to be optimal here, but it feels a computationally heavy problem.
OvervCW
In what way is it NP-hard? From what I can gather it just eliminates nodes where the pod wouldn't be allowed to run, calculates a score for each and then randomly selects one of the nodes that has the lowest score, so trivially parallelizable.
sandGorgon
does anyone know the size at openai ? it used to run a 7500 node cluster back in 2021 https://openai.com/index/scaling-kubernetes-to-7500-nodes/
yanhangyhy
there is a doc about how to do with 1M nodes: https://bchess.github.io/k8s-1m/#_why
so i guess the title is not true?
arccy
That's simulated using kwok, not real.
> Unfortunately running 1M real kubelets is beyond my budget.
Thaxll
THis is a PoC not backed by a reliable etcd replacement.
blurrybird
AWS and Anthropic did this back in July: https://aws.amazon.com/blogs/containers/amazon-eks-enables-u...
cowsandmilk
That is 100k vs 130k for Google’s new announcement. I can’t speak as to whether the additional 30k presented new challenges though.
Cthulhu_
I want to believe that this is an order-of-magnitude kind of problem, that is, if 100K is fine then 500K is also fine.
I only skimmed the article though, but I'm confident that it's more a physical hardware, time, space and electricity problem than a software / orchestration one; the article mentions that a cluster that size needs to be multi-datacenter already given the sheer power requirements (2700 watts for one GPU in a single node).
supportengineer
Imagine a Beowulf cluster of these
xyse53
They mention GCS fuse. We've had nothing but performance and stability problems with this.
We treat it as a best effort alternative when native GCS access isn't possible.
dijit
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.
thundergolfer
AWS Lambda uses FUSE and that’s one of the largest prod systems in the world.
dijit
An option exists, but they prefer you use the block storage API.
blamestross
I worked in DHTs in grad school. I still double take that Google and other companies "computers dedicated to a task" numbers are missing 2 digits from what I expected. We have a lot of room left for expansion, we just have to relax centralized management expectations.
rvz
> While we don’t yet officially support 130K nodes, we're very encouraged by these findings. If your workloads require this level of scale, reach out to us to discuss your specific needs
Obviously this is a typical experiment at Google on running a K8s cluster at 130K nodes but if there is a company out their that "requires" this scale, I must question their architecture and their infrastructure costs.
But of course someone will always request that they somehow need this sort of scale to run their enterprise app. But once again, let's remind the pre-revenue startups talking about scale before they hit PMF:
Unless you are ready to donate tens of billions of dollars yearly, you do not need this.
You are not Google.
game_the0ry
> 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.
jcims
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.
Tostino
You have jobs running on more than 130k different machines daily??
Are they cloud based VMs, or your own hardware? If cloud based, do you reprovision all of them daily and incur no cost when you are not running jobs? If it's your own hardware, what else do you do with it when not batch processing?
jcims
They are provisioned on demand (cloud) and shut down when no longer needed.
mlnj
>You are not Google.
It's literally Google coming out with this capability and how is the criticism still "You are not Google"
Rastonbury
The criticism is at pre-PMF startups who believe they need something similar
jakupovic
Doing this at anything > 1k nodes is a pain in the butt. We decided to run many <100 nodes clusters rather than a few big ones.
kvrty
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.
liveoneggs
Same. The control plane and various controllers just aren't up to the task.
preisschild
Meh, I've had had clusters with close to 1k nodes (w/ cilium as CNI) and didnt have major issues
belter
130k nodes...cute...but can Google conquer the ultimate software engineering challenge they warn you about in CS school? A functional online signup flow?
chrisandchris
The could team up with Microsoft, because their signup flow is fine but the login flow is badly broken.
jasonvorhe
For what? Access to the control plane API?
belter
In general... Try to sign up for their AI services...
blinding-streak
Imagine a Beowulf cluster of these
K8S clusters on VMs strike me as odd.
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?
And if you insist anyway, run a few big VMs rather than many small ones, since K8s overhead is per-node.