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.

Debug both standard applications and Ginkgo tests (this is what I'm using at the moment 🤷)

Let's explore a simple test, as it looks now in the code base (the code uses Ginkgo and Gomega, a somewhat overlooked combination IMHO)

Take a look at the Ginkgo testing framework to see the horrors of shoe horning FP into Go

https://onsi.github.io/ginkgo/#writing-specs

It's the same with the Ginkgo testing library: "After the package books_test declaration we import the ginkgo and gomega packages into the test's top-level namespace by performing a . dot-import. Since Ginkgo and Gomega are DSLs this makes the tests more natural to read."

https://onsi.github.io/ginkgo/

The ginkgo testing framework helps us in building the test suite. It's entirely written in Go. Furthermore, it provides a CLI utility to set up and run the tests. Since we will use it later, let's download it from here. You can download it in two ways:

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.