[rgbrgb]

In the past few years I've been using a "dirty db" approach to testing where I run parallel integration tests against a single postgres-based backend without any cleanup between tests. Every test hits the same db with unique ID's. The constraint is that you can't assert exact counts, you need to assert that specific ID's exist. With this setup, the db just gets setup once and all the tests can run in parallel. Integration points are where I've seen the most breakage in prod, so I like to test with a real db. Parallelism makes it fast and incidentally this approach sometimes catches racy interactions that otherwise might only see with concurrent prod requests (heisenbugs in waiting). Many tests hitting the API's in parallel ends up being a better simulation of prod behavior.

[arialdomartini]

My team and I are using the same approach since about 5 years. I collected a bit of notes about it here

https://arialdomartini.github.io/when-im-done-i-dont-clean-u...

[benoau]

I like this approach too, just have to build your tests around the expectation that there will be unrelated/residual test data. This is also very useful when you're testing the frontend too like Playwright tests where cleaning up data requires helper endpoints or db connection info and libraries.

What I really don't like is mocking dependencies and if a function gets called you pass, because so much more can actually go wrong like transaction locks, SQL issues, data issues, UI issues.

[jtwaleson]

Same approach here. Sometimes it's hard to debug. "Why is this weird test failing for an unrelated change", but I have to remind myself that it's a real concurrency bug that would have otherwise gone unnoticed. Worth the effort in my book!