Just to throw my anecdote in: I used to work at the mypy shop - our client code base was on the order of millions of lines of very thorny Python code. This was several years ago, but to the best of my recollection, even at that scale, mypy was nowhere near that slow.

Like I said, this was many years ago - mypy might've gotten slower, but computers have also gotten faster, so who knows. My hunch is still that you have an issue with misconfiguration, or perhaps you're hitting a bug.

My current company is a Python shop, 1M+ LOC. My CI run earlier today completed mypy typechecking in 9 minutes 5 seconds. Take from that what you will.

Ditto, same order of magnitude experience; at least for --no-incremental runs.

Part of the problem for me is how easily caches get invalidated. A type error somewhere will invalidate the cache of the file and anything in its dependency tree, which blows a huge hole runtime.

Checking 1 file in a big repo can take 10 seconds, or more than a minute as a result.

I guess that there is something with the cache that we don't do right. Thanks for your return.

Never happened for me. Similarly sized code base, done in seconds, if not 1s. Guess we all have our anecdotes.

I think you have something misconfigured, or are timing incorrectly. I'm working on a project right now with ~10K LOC. I haven't timed it, but it's easily <= 2 seconds. Even if I nuke MyPy's cache, it's at most 5 seconds. This is on an M3 MBP FWIW.

And with dmypy (included with myoy) it’s even faster

I've found dmypy very underbaked. It's very easy to get it to regularly crash or pin a CPU indefinitely in my codebase.

Yeah it’s far from perfect, but speed is usually not its biggest fault.

I’ll still be switching to the astral offering as soon as it’s production ready.