SQL is fine. We use it for some things. But not writing SQL is easier than writing SQL. Our data is small enough to fit in memory. Having all the data in memory and just accessible is easier than doing SQL + network round trips to get anything.
ORMs: consider yourself lucky. They try to make SQL easy by auto-generating terrible SQL.
Testing latency: we want to run many unit tests very quickly without high start-up cost. Launching MySQL/PostgreSQL docker containers and running tests against Real Databases is slower than we'd like.
Dependencies: Docker and those MySQL or PostgreSQL servers in containers.
Can you put some numbers on how much time is too much? I've never seen anyone go this far to avoid using a database for what sounds like the only "real" reason is to avoid testing latency (a problem which has many other solutions) so I am really confused, but curious to understand!
Running all of our control server tests (including integration tests) right now takes 8 seconds, and we're not even incredibly happy with that. There's no reason it should even be half that.
So it's not really in our patience budget for adding a mysqld or postgres start up (possible docker pull, create its schema, etc).
>right now takes 8 seconds, and we're not even incredibly happy with that
With the amount of explaining and skepticism you're having to deal with in most of the threads here (plenty of reasonable questions, some seem to approach the question with the assumption that your approach is totally wrong) I feel compelled to comment on how nice such a fast feedback loop would be just so it's known that you're not listing these benefits into an ether that doesn't appreciate them.
Not sure what their requirements are, but I'm using a "spin up an isolated postgres instance per test run" solution and end up with ~3s overhead to do that. (Using https://pypi.org/project/testing.postgresql/
Edit: 3s for global setup/teardown. Not per test function/suite.
Docker itself doesn't add much latency. It just makes getting MySQL and PostgreSQL easier. If anything, it helps with dependencies. The database server startup still isn't great, though.
If you don't use Docker, you can just leave the database server running in the background, which removes the startup latency (you can of course do this with Docker too, but Docker has a tendency to use quite a few resources when left running in the background, which a database server on it's own won't).
I mean that's an `apt install postgres` or `brew install postgres` away. Takes about 5 minutes. I guess it could become a pain if you need to work with multiple different versions at once.
Being deep in the cloud world right now, with aws and terraform and kubernetes cli tools, etc, not having to install third party tools on my machine does sound pretty great, but also entirely unrealistic.
Managing local DBs once new versions are out and your server isn't upgraded yet is irritating, but when I'm using a Mac I'd still rather use a native DB than Docker because of the VM overhead, since I've not yet run into a bug caused by something like "my local postgres was a different version than the server was." (Closest I've gotten was imagemagick for mac doing something a bit differently than for linux, about 10 years ago at this point.)
> I've not yet run into a bug caused by something like "my local postgres was a different version than the server was."
Ran into that at a recent place - the code was doing "= NULL" in a bunch of places (before my time) and PG12 treated that differently than PG11 did which broke a bunch of tests.
Were you doing a lot of logic in SQL itself? Sounds like not really, but then I'm surprised you'd have so many tests hitting the DB directly, vs most feature logic living above that layer in a way that doesn't need the DB running at all.
Why are your unit tests touching a database? I’m a real stickler about keeping unit tests isolated, because once I/O gets involved, they invariably become much less reliable and as you mention, too slow.
Sorry, I should've just said tests. Our tests overall are a mix of unit tests and integration tests and all sizes in between, depending on what they want to test.
ORMs: consider yourself lucky. They try to make SQL easy by auto-generating terrible SQL.
Testing latency: we want to run many unit tests very quickly without high start-up cost. Launching MySQL/PostgreSQL docker containers and running tests against Real Databases is slower than we'd like.
Dependencies: Docker and those MySQL or PostgreSQL servers in containers.