Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/pydantic/pydantic-core
Core validation logic for pydantic written in rust
https://github.com/pydantic/pydantic-core
json-schema parsing pydantic rust schema validation
Last synced: 13 days ago
JSON representation
Core validation logic for pydantic written in rust
- Host: GitHub
- URL: https://github.com/pydantic/pydantic-core
- Owner: pydantic
- License: mit
- Created: 2022-04-04T22:36:55.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-22T21:13:22.000Z (6 months ago)
- Last Synced: 2024-05-22T22:23:40.457Z (6 months ago)
- Topics: json-schema, parsing, pydantic, rust, schema, validation
- Language: Python
- Homepage:
- Size: 6.41 MB
- Stars: 1,289
- Watchers: 28
- Forks: 194
- Open Issues: 75
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# pydantic-core
[](https://github.com/pydantic/pydantic-core/actions?query=event%3Apush+branch%3Amain+workflow%3Aci)
[](https://codecov.io/gh/pydantic/pydantic-core)
[](https://pypi.python.org/pypi/pydantic-core)
[](https://github.com/pydantic/pydantic-core)
[](https://github.com/pydantic/pydantic-core/blob/main/LICENSE)
This package provides the core functionality for [pydantic](https://docs.pydantic.dev) validation and serialization.
Pydantic-core is currently around 17x faster than pydantic V1.
See [`tests/benchmarks/`](./tests/benchmarks/) for details.
## Example of direct usage
_NOTE: You should not need to use pydantic-core directly; instead, use pydantic, which in turn uses pydantic-core._
```py
from pydantic_core import SchemaValidator, ValidationError
v = SchemaValidator(
{
'type': 'typed-dict',
'fields': {
'name': {
'type': 'typed-dict-field',
'schema': {
'type': 'str',
},
},
'age': {
'type': 'typed-dict-field',
'schema': {
'type': 'int',
'ge': 18,
},
},
'is_developer': {
'type': 'typed-dict-field',
'schema': {
'type': 'default',
'schema': {'type': 'bool'},
'default': True,
},
},
},
}
)
r1 = v.validate_python({'name': 'Samuel', 'age': 35})
assert r1 == {'name': 'Samuel', 'age': 35, 'is_developer': True}
# pydantic-core can also validate JSON directly
r2 = v.validate_json('{"name": "Samuel", "age": 35}')
assert r1 == r2
try:
v.validate_python({'name': 'Samuel', 'age': 11})
except ValidationError as e:
print(e)
"""
1 validation error for model
age
Input should be greater than or equal to 18
[type=greater_than_equal, context={ge: 18}, input_value=11, input_type=int]
"""
```
## Getting Started
You'll need rust stable [installed](https://rustup.rs/), or rust nightly if you want to generate accurate coverage.
With rust and python 3.8+ installed, compiling pydantic-core should be possible with roughly the following:
```bash
# clone this repo or your fork
git clone [email protected]:pydantic/pydantic-core.git
cd pydantic-core
# create a new virtual env
python3 -m venv env
source env/bin/activate
# install dependencies and install pydantic-core
make install
```
That should be it, the example shown above should now run.
You might find it useful to look at [`python/pydantic_core/_pydantic_core.pyi`](./python/pydantic_core/_pydantic_core.pyi) and
[`python/pydantic_core/core_schema.py`](./python/pydantic_core/core_schema.py) for more information on the python API,
beyond that, [`tests/`](./tests) provide a large number of examples of usage.
If you want to contribute to pydantic-core, you'll want to use some other make commands:
* `make build-dev` to build the package during development
* `make build-prod` to perform an optimised build for benchmarking
* `make test` to run the tests
* `make testcov` to run the tests and generate a coverage report
* `make lint` to run the linter
* `make format` to format python and rust code
* `make` to run `format build-dev lint test`
## Profiling
It's possible to profile the code using the [`flamegraph` utility from `flamegraph-rs`](https://github.com/flamegraph-rs/flamegraph). (Tested on Linux.) You can install this with `cargo install flamegraph`.
Run `make build-profiling` to install a release build with debugging symbols included (needed for profiling).
Once that is built, you can profile pytest benchmarks with (e.g.):
```bash
flamegraph -- pytest tests/benchmarks/test_micro_benchmarks.py -k test_list_of_ints_core_py --benchmark-enable
```
The `flamegraph` command will produce an interactive SVG at `flamegraph.svg`.
## Releasing
1. Bump package version locally. Do not just edit `Cargo.toml` on Github, you need both `Cargo.toml` and `Cargo.lock` to be updated.
2. Make a PR for the version bump and merge it.
3. Go to https://github.com/pydantic/pydantic-core/releases and click "Draft a new release"
4. In the "Choose a tag" dropdown enter the new tag `v` and select "Create new tag on publish" when the option appears.
5. Enter the release title in the form "v "
6. Click Generate release notes button
7. Click Publish release
8. Go to https://github.com/pydantic/pydantic-core/actions and ensure that all build for release are done successfully.
9. Go to https://pypi.org/project/pydantic-core/ and ensure that the latest release is published.
10. Done 🎉