This is an awesome experiment and write up. I really appreciate the reproducibility.
I would like to see how moving to database that scales write throughput with replicas would behave, namely FoundationDB. I think this will require more than an intermediary like kine to be efficient, as the author illustrates the apisever does a fair bit of its own watching and keeping state. I also think there's benefit, at least for blast radius, to shard the server by api group or namespace.
I think years ago this would have been a non starter with the community, but given AWS has replaced etcd (or at least aspects) with their internal log service for their large cluster offering, I bet there's some appetite for making this interchangable and bringing and open source solution to market.
I share the authors viewpoint that for modern cloud based deployments, you're probably best avoiding it and relying on VMs being stable and recoverable. I think reliability does matter if you want to actually realize the "borg" value and run it on bare metal across a serious fleet. I haven't found the business justification to work on that though!
It's definitely a different use case but given they haven't had to tap into their follower replicas for scale, it must be pretty efficient and lightweight. I suspect not having ACLs helps. They also cite a minimum 2MB size, so not expecting exabtyes of little bytes.
I wonder if a major difference is listing a prefix in object storage vs performing recursive listings in a file system?
Even in S3, performing very large lists over a prefix is slow and small files will always be slow to work with, so regular compaction and catching file names is usually worthwhile.
It would be great to see where the limits are with this approach.
I think at some point, you need to go deeper into the apiserver for scale than an API compatible shim, but this is just conjecture and not real data.