GitHub - pydantic/pydantic-core: Core validation logic for pydantic written in rust (original) (raw)

pydantic-core

This package provides the core functionality for pydantic validation and serialization.

Pydantic-core is currently around 17x faster than pydantic V1. See 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.

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, or rust nightly if you want to generate accurate coverage.

With rust and python 3.9+ installed, compiling pydantic-core should be possible with roughly the following:

clone this repo or your fork

git clone git@github.com: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 andpython/pydantic_core/core_schema.py for more information on the python API, beyond that, 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. (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.):

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<the.new.version> and select "Create new tag on publish" when the option appears.
5. Enter the release title in the form "v<the.new.version> "
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 🎉