Cache pydantic model fields for faster decoding #132
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
In `timeit` tests, 10.000 calls to `ModelField()` could take up to half a second on my Macbook Pro M1, depending on the type annotation used. Given that the method is called for every cache hit, this can really add up. The number of different return types for endpoints is very much finite however, so caching is a definite win here.
mjpieters
force-pushed
the
cache_pydantic_fields
branch
from
6382ee9 to
7c30402
Compare
|
Thanks! |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
In
timeittests, 10.000 calls toModelField()could take up to half a second on my Macbook Pro M1, depending on the type annotation used. Given that the method is called for every cache hit, this can really add up. The number of different return types for endpoints is very much finite however, so caching is a definite win here.