pixie VS enhancements

I am really curious, why build another project that has similar features as another open source software pixie - https://px.dev/.

Kibana is a good alternative if you can allow storing all your data in Elasticsearch, or you can use all-in-one monitoring tools like pixie https://github.com/pixie-io/pixie

Plenty of paid monitoring solutions out there. Instana is pretty slick. NewRelic has a new open source tool, https://github.com/pixie-io/pixie

Cilium multi-cluster mesh is a bit different, compared to service meshes, and it's L4, so should be fine with eBPF / XDP based routing. From observability standpoint I'd go for pixie - it should work just fine with both linkerd and istio, instead of Hubble, and Cilium's Tetragon was missing policies about 3-4 months ago (needs doublec-hecking if they had introduced any).

Congratulations on the launch, and thank you for choosing an awesome license!

For an unrelated reason, today I was reminded about Pixie (https://news.ycombinator.com/item?id=25375170 and https://news.ycombinator.com/item?id=31687978 and https://github.com/pixie-io/pixie#readme ), which says is also an ebpf kubernetes observability tool, also Apache licensed.

I suspect the difference may be your aspirations to move out of just kubernetes, but I wondered if that's the biggest difference between your project and theirs? Or maybe the C++ versus golang?

eBPF solutions look cool though. I heard of https://px.dev/ which sounds similar to ciliums eBPF mesh recently.

0 is not the request we've defined. And that makes sense. Memory QoS has been in alpha since Kubernetes 1.22 (August 2021) and according to the KEP data was still in alpha as of 1.27.

Note: There's actually a Structured Authentication Config established via KEP-3331. It's in v1.28 as a feature flag gated option and removes the limitation of only having one OIDC provider. I may look into doing an article on it, but for now I'll deal with the issue in a manner that should work even with a bit older versions versions of Kubernetes.

Kubernetes supports a skew policy of n+2 between API server and kubelet. This means if your CP and DP are both on 1.20, you could upgrade your control plane twice (1.20 -> 1.21 -> 1.22) before you need to upgrade your data plane. And when it comes time to upgrade your data plane you can jump from 1.20 to 1.22 to minimize update churn. In the future, this skew will be opened to n+3 https://github.com/kubernetes/enhancements/tree/master/keps/sig-architecture/3935-oldest-node-newest-control-plane

The KEP (Kubernetes Enhancement Proposal) is linked to in the PR [1]. From the summary:

> Sidecar containers are a new type of containers that start among the Init containers, run through the lifecycle of the Pod and don’t block pod termination. Kubelet makes a best effort to keep them alive and running while other containers are running.

[1] https://github.com/kubernetes/enhancements/tree/master/keps/...

This is where the new feature of mutable scheduling directives for jobs comes into play. This feature enables the updating of a job's scheduling directives before it begins. Essentially, it allows custom queue controllers to influence pod placement without needing to directly handle the assignment of pods to nodes themselves. To learn more about this check out the Kubernetes Enhancement Proposal 2926.

What your asking is a (vanilla) Kubernetes non-goal, others have mentioned fluxcd and other add ons that provide primitives for dependency aware deployments. The problem space is so large, that it's unreasonable to to address these concerns in Kubernetes itself, instead, make it extensible... Look at this KEP for example: https://github.com/kubernetes/enhancements/issues/753 Sidecar containers have existed, and been named as such since WAY before that KEP's inception, defining what these things should and shouldn't do is largely arbitrary. Aka: your use-case is niche, if you don't like the behavior, use flux or argo, or write something yourself.

If further interested, I may recommend checking out the KEP. I love how they document the decision making, and all these edge cases :).

See https://kubernetes.io/docs/reference/kubectl/conventions/#subresources and https://github.com/kubernetes/enhancements/issues/2590