cel-spec VS microk8s

My employer uses a combination of Protocol Buffers (for the config schema definition) and Bazel/Starlark (for concrete instantiations). Configs are validated at build time and runtime using CEL (https://github.com/google/cel-spec).

The Common Expression Language (CEL) implements common semantics for expression evaluation, enabling different applications to more easily interoperate. https://github.com/google/cel-spec

Looks like Google invented a specification for a simple "expression language." -> https://github.com/google/cel-spec/blob/master/doc/langdef.md. Writing the expressions feels like writing Java, C++, Go, or TypeScript code. Google then released C++ and Go versions of this langauge as a library.

https://github.com/google/cel-spec unfortunately, it's in Go or C++. Of course I can write a binding to them. But is there any other similar that you would know of for Java? My other course of action would be to offload computation to another service using this library in Go, or Jsonnet or Open Policy Agent/Rego based evaluation, which I'd prefer not to. Executing JS in Java via Nashorn also an option but it'd be heavy weight.

Having a standard way to share expressions does seem quite useful, especially when it's multilingual.

[0]: https://github.com/google/cel-spec

There's more information about CEL and its specifications here

Especially when dealing with more complex match rules, I personally much prefer Caddy's matchers over building some weird-ass if constructs in Nginx. It also supports CEL for request matching, giving you access to extremely powerful logic, if you need it.

If you want hands-on practice you should have a running Kubernetes cluster (I used MicroK8s for this tutorial) and Helm (see how to install on Installing Helm tutorial). It is important that you understand the basics of these tools to fully understand.

And install microk8s:

MicroK8s is a lightweight, batteries included Kubernetes distribution by Canonical designed for running edge workloads which also happens to be developer-friendly and a great choice for building your own homelab. The following lab covers how to install and run MicroK8s on your own edge node running Ubuntu 22.04 LTS, deploy the NGINX web service and exposing your NGINX website to the Internet with SSL/TLS enabled using AWS resources included within the Free Tier.

One quick and easy win I can recommend, is microk8s.

I had the same problem (and there's a github issue about this: https://github.com/canonical/microk8s/issues/2186). I swapped to k3s and the usage was half of what microk8s used.

microk8s currently has a showstopping issue that makes it guaranteed to have an irrecoverable failure in HA mode. see https://github.com/canonical/microk8s/issues/3227

k0s is better but also has a lot of bugs. it's the closest to vanilla kubernetes among all the distributions.

> like the simplest GPU support

linux users should be ready to install the nvidia device plugin. if they can't do that, they're never going to succeed in running a gpu accelerated application on their cluster anyway.

> like bootstrapping

in my experience, writing all the bootstrap scripts is painful. but now that there's chatgpt, so much of the drudgery as gone away.

MicroK8S to spin up a Kubernetes cluster

They now power my microk8s/x86 cluster (in addition to my 8-node Raspberry Pi4 ARM64 microk8s cluster), microceph cluster and my LXD cluster, and all are configured with WOL, so I can bring up the cluster from any machine in the homelab, on demand.