go-cmp VS ginkgo

This made me think of a couple other interesting things:

1. you can change which algorithm is used in git diff as multiple are supported

https://luppeng.wordpress.com/2020/10/10/when-to-use-each-of...

2. Google has an edit graph implementation in Go in the cmp package

https://github.com/google/go-cmp/blob/master/cmp/internal/di...

Assertion libraries that people seem to love: - testify (my favorite) - go-cmp is a more barebones library - gotest.tools -- I have never used this but some swear by it

it uses https://github.com/google/go-cmp instead of reflect.DeepEqual

When I use functional arguments, I either prefix all of the options with the same prefix or put them in a dedicated package (like cmpopts) to help the IDE.

Checking for equality in tests: https://github.com/google/go-cmp

oklog/ulid to generate IDs. coreos/go-oidc for validating JWTs I get from auth. google/go-cmp for comparing structs in tests (unless the project is already using Testify). spf13/pflag because life's too short for Go's flag handling. getkin/kin-openapi for validating reqests/responses against my OpenAPI spec (in tests).

One thing to keep in mind about reflect.DeepEqual is because of the way it is implemented you could get positive results when the values are not actually the same, see this comment for reference.

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.

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/