upspin VS zap

Super intriguing. Thanks for sharing!

It reminds me a bit of an early Go project called Upspin [1]. And also a bit of Solid [2]. Did you get any inspiration from them?

What excites me about your project is that you're addressing the elephant in the room when it comes to data sovereignty (~nobody wants to self-host a personal database but their personal devices aren't publicly accessible) in an elegant way.

By storing the data on my personal device and (presumably?) paying for a managed relay (and maybe an encrypted backup), I can keep my data in my physical possession, but I won't have to host anything on my own. Is that the idea?

https://upspin.io/

There are a few Go projects meant to be learned from:

- https://github.com/pion/opus for to learn audio

- https://github.com/benbjohnson/wtf for overall production quality

- https://github.com/upspin/upspin difficult to explain, personally I'm not a fan of the errors

You could also take a look at some real-world open-source projects. I like upspin for its idiomatic approach.

For example, Rob Pike's upspin places all its validations in the separate package. Do you agree with that approach? Which yet proven options there are?

Just a few projects that could perhaps interest you in terms of design of your own solution :

Upspin: https://upspin.io/

The early error wrapping work which emerged out of the Upspin project, that eventually made its way into the errors package, included stack traces in the wrap error. This would provide exactly what it appears you seek.

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.