ginkgo VS zap

Ginkgo: a testing framework based on the concept of ‌"Behavior Driven Development" (BDD)

We were also able to leverage Ginkgo's parallel testing runtime to run our integration tests on multiple concurrent processes. This provided multiple benefits: we could run our entire integration test suite in under 10 minutes and also reuse the same suite to load test the operator in a production-like environment. Using these tests, we were able to identify hot spots in the code that needed further optimization and experimented with ways to save API calls to ease the load on our own Kubernetes API server while also staying under various AWS rate limits. It was only after running these tests over and over again that I felt confident enough to deploy the operator to our dev and prod clusters.

A bit off-topic, but i really like the ginkgo BDD framework

You obviously are not familiar with the third circle of golang continuous integration hell that is ginkgo+gomega:

https://onsi.github.io/ginkgo/#adding-specs-to-a-suite

It’s actually worse than that example suggests. Stuff like Expect(“type safety”).ShouldBe(GreaterThan(13)) throws runtime errors.

The semantics of parallel test runs weren’t defined anywhere the last time I checked.

Anyway, you’ll be thinking back fondly to the days of TestShouldReplaceChildrenWhenUpdatingInstance because now you need to write nested function calls like:

Context(“instances”, func …)

Describe(“that are being updated”, …)

Expect(“should replace children”, …)

And to invoke that from the command line, you need to write a regex against whatever undocumented and unprinted string it internally concatenates together to uniquely describe the test.

Also, they dump color codes to stdout without checking that they are writing to a terminal, so there will be line noise all over whatever automated test logs you produce, or if you pipe stdout to a file.

If you are using Ginkgo for your e2e, this library might of help.

https://onsi.github.io/ginkgo/ offers a simple way to create tables with different scenarios useful to generate different test cases based on a file like a yml without to need to develop useless code. Maybe at start seems to be a little verbose but depends how you design the test case.

What are people’s thoughts on testing frameworks? I’ve heard that most devs only use the testing package in the standard library and the testify package for assertions— I assume this is because Go is meant to be lightweight and scalable, and adding external dependencies basically goes against that. But I’ve also seen devs use packages like ginkgo to make tests more structured and readable. What do you guys think?

Ginkgo Behavioural test framework

If you write your tests with Ginkgo [0] its CLI can do this for you. It also has nice facilities to quickly disable a test or portion of a test by pretending an X to the test function name, or to focus a test (only run that test) by prepending an F. It’s pretty nice.

[0]: https://onsi.github.io/ginkgo/

The BDD testing framework Ginko [1] has some "weird" / unidiomatic patterns, yet it is very popular

https://github.com/onsi/ginkgo

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.