gomega VS ginkgo

Gomega: is a test assertion library, a vital dependency on Ginkgo.

Gomega is a Go library that allows you to make assertions. In our example, we check if what we got is null, not null, or equal to an exact value, but the gomega library is much richer than that.

Depends on the API and how much testing you need. You want to test your code, not the API's availability or correctness.

But it can be as easy as using a fake http library and mocking the responses, or using a httptest server: https://onsi.github.io/gomega/#ghttp-testing-http-clients

If the API is complicated and you have to write your own fake server, that might not make sense for small projects.

If we do the count, we gather that subtest appear to solve five out of the six problems we identified with the assert library. At this point though, it's important to note that at the time when the assert package was designed, the sub-test feature in Go did not yet exist. Therefore it would have been impossible for that library to embed it into it's design. This is also true for when Gomega and Ginko where designed. If these test frameworks where created now, then most likely some parts of their design would have been done differently. What I am trying to say is that with even the slightest change in the Go language and standard library, completely new ways of designing programs become possible. Especially for new packages without any legacy use-cases to consider. And this brings us to generics.

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

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.

If you like BDD and more expressive tests, go get ginkgo. It is a pain in the back get the hang of its R-spec like syntax, but if you're good at it and you like it, it is very expressive and readable. It has its own matcher (assertions library), gomega, but if you don't like it, you can still use testify instead.

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