Description
Bug Report
For Pulumi we are looking into generating types using TypedDict to model cloud APIs. For example for Kubernetes we have something representing a Deployment.
class DeploymentArgsDict(TypedDict):
api_version: NotRequired[Input[str]]
kind: NotRequired[Input[str]]
metadata: NotRequired[Input['ObjectMetaArgsDict']]
...Pulumi has a notion of inputs and outputs, and the Input type used in the above example looks like this:
Input = Union[T, Awaitable[T], Output[T]]
class Output(Generic[T]):
passOutput does a lot things, but for the purposes of this repro all that matters is that its a generic type.
The K8S types can nest pretty deeply, and I suspect a combination of having nested literals along with the Union via the Input type is causing slowness here.
Example:
d: DeploymentArgsDict = {
"metadata": {
"name": "nginx",
},
"spec": {
"selector":{
"match_labels": {}
},
"replicas": 1,
"template": {
"metadata": {
"labels": {}
},
"spec": {
"containers": [{
"name": "nginx",
"image": "nginx"
}]
}
}
}
}
If I drop Awaitable[T] from the union to reduce it to two members, typechecking completes in 2 seconds. With it present, it takes 40 seconds.
This is a simplified example, and the actual code has another union layered on top. In that case we run out of memory.
To Reproduce
I have created a repro here https://github.com/julienp/typeddict-performance
Expected Behavior
It takes a second or two to typecheck.
Actual Behavior
It takes ~40 seconds on my machine
Your Environment
- Mypy version used: 1.10
- Mypy command-line flags: none
- Mypy configuration options from
mypy.ini(and other config files): none - Python version used: 3.12.2