dockertest VS venom

I am using https://github.com/ory/dockertest for tests, specifically for databases. Is there any advantage to use Testcontainers?

Now, let's run the tests. For this purpose, we are going to use dockertest, but test containers is also a good solution.

During my path learning go so far I have come across some amazing libraries and utilites, one of my favourite for integration testing is dockertest.

Things I can't live without in a new Go project in no particular order:

- https://github.com/golangci/golangci-lint - meta-linter

- https://goreleaser.com - automate release workflows

- https://magefile.org - build tool that can version your tools

- https://github.com/ory/dockertest/v3 - run containers for e2e testing

- https://github.com/ecordell/optgen - generate functional options

- https://golang.org/x/tools/cmd/stringer - generate String()

- https://mvdan.cc/gofumpt - stricter gofmt

- https://github.com/stretchr/testify - test assertion library

- https://github.com/rs/zerolog - logging

- https://github.com/spf13/cobra - CLI framework

FWIW, I just lifted all the tools we use for https://github.com/authzed/spicedb

We've also written some custom linters that might be useful for other folks: https://github.com/authzed/spicedb/tree/main/tools/analyzers

I've used dockertest a bunch and it is really amazing.

Reminds me of https://github.com/ory/dockertest

If your project is relatively simple and you can get away with recreating your scenarios against a real mock database and run other related services locally. It would be good to setup docker containers for your test scripts and write e2e tests. I believe e2e tests are harder but more useful in understanding/reasoning how users are impacted.

Just a heads up, the repository in your comment is invalid, the correct link is https://github.com/ory/dockertest

Don't mock. Use https://github.com/ory/dockertest to actually run tests against a dockerized DB.

Use a library like ory/dockertest to spin up a test database for integration tests. It's easy to use, and tests are still fast. It'll take a minute to download the mysql docker image the first time. But, once it's been downloaded, starting the db, running migrations, and running the tests is still pretty quick.

You can also load fixtures in database directly, work with Kafka queues both as a producer (e.g. write an event to a Kafka queue, wait a few seconds and see that it was consumed by the service you test, and that some side effects can be observed) or as a consumer (e.g. make sure after an HTTP call, an event was correctly pushed to a queue), or even read a mailbox in IMAP to check that your service correctly send an email.

It's a bit rough on the edges sometimes, but I'd never go back on writing integration tests directly in my programming language. Declarative is the way to go.

[1]: https://github.com/ovh/venom

To write and run our integration tests, we'll use Venom. Venom is a tool created and made open-source by OVHcloud: https://github.com/ovh/venom

From my experience, generated tests are worthless for anything more serious than smoke tests. I prefer working with no tests than automated tests, I feel they give you a false sense of confidence.

The Step CI engine itself looks good though. It looks like a cleaner, but less powerful version of a tool (open source, build in-house) we used when I worked at OVHcloud, Venom: https://github.com/ovh/venom

Here's an example test file for the HTTP executor of Venom: https://github.com/ovh/venom/blob/master/tests/http.yml it's very close to Step CI format.

I'd still use Venom because it's way more powerful (you have DB executors for example, so after executing a POST request you can actually check in DB that you have what you expect) and I prefer focusing on actually writing integration tests instead of generating them.

Maybe this post sounds harsh (I feel it as I write it because I have strong feelings against test generation) but I think your approach is a good one for actually writing automated tests. Testing APIs declaratively like this has a great benefit: your tests work on an interface. You can migrate your API to a whole new stack and your tests remain the same. I did it multiple time at OVHcloud: one time migrating a huge API from a Go router to another (Gin->Echo), and another time migrating public APIs from a legacy, in-house Perl engine to a Go server.

I still struggle with GDB but my excuse is that I seldom use it.

When I was studying reverse engineering though, I came across a really cool kit (which I've yet to find an alternative for lldb, which would be nice given: rust)

I'd recommend checking it out, if for no other reason than it makes a lot of things really obvious (like watching what value lives in which register).

https://github.com/hugsy/gef

LLDB's closest alternative to this is called Venom, but it's not the same at all. https://github.com/ovh/venom

We incorporated [Venom](https://github.com/ovh/venom) into our workflow. It's great for initiating and managing a suite of yaml based tests. It didn't work out of the box for us due to the heavily asynchronous nature of our system, but after a few additions, it has helped my team greatly. We were often afraid to make large changes to critical pieces of the system since a full regression test could take a week or so to check everything. Now it takes an hour.