zap VS GORM

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.

GORM is a comprehensive ORM tool in Go, offering a code-first approach which allows defining database schemas using struct tags in Go. It's known for its developer-friendly nature, making it suitable for both beginners and experienced users. GORM supports a variety of SQL databases like MySQL, PostgreSQL, and SQLite. It's designed to be flexible, allowing developers to drop down to raw SQL when necessary. However, it's important to be cautious about its performance implications in large-scale applications.

Homepage: https://gorm.io/

Since most of these APIs will be simple CRUD (Create/Read/Update/Delete) endpoints, let's build this service using GORM, an ORM library that makes building CRUD endpoints really simple.

After successfully configuring the application, it's time to delve into integrating the data layer. For this purpose, I will utilize gorm, a powerful SQL ORM that facilitates rapid development of the data layer using model structs.

For basic INSERT ... VALUES ... or SELECT ... WHERE ... JOIN ..., use a library such as GORM. For INSERT .... SELECT ... statements where one combines two round trips (SELECT and INSERT) into one, ORMs have a hard time performing this query. Particularly when you start doing joins. Joins are the heart of Relational database theory (they denote relationships). So get to know what an INNER, LEFT, RIGHT and OUTER join is and why you would use them. Also learn INSERT ... SELECT ...

I have never hated gorm and it serves me well. However I tend to feed it raw SQL very often.

Array of values with embedded value all pointed to the last value, reflect code was broken: https://github.com/go-gorm/gorm/pull/5901 data corruption