kubebuilder VS zap

The Spin operator uses the Kubebuilder framework and contains a Spin App Custom Resource Definition (CRD) and controller. It watches Spin App Custom Resources and realizes the desired state in the K8s cluster. Aside from the immediate benefits gained by running Wasm workloads in k8s, additional optimizations such as Horizontal Pod Scaling (HPA) and k8s Event-driven Autoscaling (KEDA) can be achieved in a pinch.

kubebuilder: brew install kubebuilder

The operator that I've been working on is designed to manage the full lifecycle of a QuestDB database instance, including version and hardware upgrades, config changes, backups, and (eventually) recovery from node failure. I used the Operator SDK and kubebuilder frameworks to provide scaffolding and API support.

Recently, I've been spending a lot of time writing a Kubernetes operator using the go operator-sdk, which is built on top of the Kubebuilder framework. This is a list of a few tips and tricks that I've compiled over the past few months working with these frameworks.

Since we built our operator using the Kubebuilder framework, most standard monitoring tasks were handled for us out-of-the-box. Our operator automatically exposes a rich set of Prometheus metrics that measure reconciliation performance, the number of k8s API calls, workqueue statistics, and memory-related metrics. We we were able to ingest these metrics into pre-built dashboards by leveraging the grafana/v1-alpha plugin, which scaffolds two Grafana dashboards to monitor Operator resource usage and performance. All we had to do was add these to our existing Grafana manifests and we were good to go!

I wrote a CSI driver and some operators. I admire K8s, because you can find solution to almost any problem in the source code - API versioning, load balancing, request throttling, optimistic concurrency, security, and much much more. I recommend https://book.kubebuilder.io/ It is similar to Operator SDK, but without Openshift-specific stuff. It gradually introduces you to many k8s concepts, and follows design patterns that k8s uses internally.

Do you mean this: https://book.kubebuilder.io/ ?

https://book.kubebuilder.io/ all you need to know

A better way to write an operator these days is to use kubebuilder [1].

My complaint is that I have seen orgs write operators for random stuff, often reinventing the wheel. Lot of operators in orgs are result of resume driven development. Having said that it often comes handy for complex orchestration.

[1]https://github.com/kubernetes-sigs/kubebuilder

The project currently uses slog package from standard library for logging. But switching to a more advanced logger like zap could offer more flexibility and features.

It's nice to have this in the standard library, but it doesn't solve any existing pain points around structured log metadata and contexts. We use zap [0] and store a zap logger on the request context which allows different parts of the request pipeline to log with things like tenantid, traceId, and correlationId automatically appended. But getting a logger off the context is annoying, leads to inconsistent logging practices, and creates a logger dependency throughout most of our Go code.

[0] https://github.com/uber-go/zap

Kubebuilder, like much of the k8s ecosystem, utilizes zap for logging. Out of the box, the Kubebuilder zap configuration outputs a timestamp for each log, which gets formatted using scientific notation. This makes it difficult for me to read the time of an event just by glancing at it. Personally, I prefer ISO 8601, so let's change it!

What else would you expect from a structured logging package?

To me it absolutely makes sense as the default and standard for 99% of applications, and the API isn't much unlike something like Zap[0] (a popular Go structured logger).

The attributes aren't an "arbitrary" concept, they're a completely normal concept for structured loggers. Groups are maybe less standard, but reasonable nevertheless.

I'm not sure if you're aware that this is specifically a structured logging package. There already is a "simple" logging package[1] in the sodlib, and has been for ages, and isn't particularly fast either to my knowledge. If you want really fast you take a library (which would also make sure to optimize allocations heavily).

[0]: https://pkg.go.dev/go.uber.org/zap

[1]: https://pkg.go.dev/log

And finally for structured logging: https://github.com/uber-go/zap

For logging: I recommend using Uber Zap https://github.com/uber-go/zap It will log stack backtraces and makes it super easy to debug errors when deployed. I typically log in the business logic and not below. And log at the entry for failures to start the system. Maybe not necessary for this example, but it’s an essential piece of any API backend.