Pydantic

What's coming in V2

&

A review of building python extensions with Rust

Please ask questions as I go along ... I've written this in a hurry so if you're confused, you're probably not alone

Some Background

Pydantic does data validation using type hints
It was first released in 2017 as a tiny experiment
The library has since seen massive growth ... particularly since Sebastián used pydantic in FastAPI. Thanks Sebastián! 🙏
Pydantic hasn't been significantly rewritten since v0.0.1
The internals are creaking
V2 is an opportunity to fix some of footguns but also re-write the internals

The good bits

Everything! (and hopefully nothing for you):

all validation is offloaded to another library - pydantic-core which I'm building now
Ugly hangovers from mistakes I made while building v0.0.1 can be killed
We can use a little of the speed premium provided by pydantic-core to do some stuff more correctly - e.g. smart unions
I've given up on the "validation" vs. "parsing" vs. "coercion" debate, I'm just using "validate" - if you don't like it 🖕 (or just use strict=True)

What's changing in V2

"You can't make a Tomlette without breaking a few Gregs!" (Succession S2E9) - best TV pun ever?

I'm going to have to upset some people to get V2 out:

a bunch of PRs will have to be closed - users can come back and restart them once the meat of the V2 changes are in main
Some functionality will change, I think for the better, but some people will disagree
Some hacks and workarounds will no longer be possible as the logic is in rust where you can't mess
no more subclasses of basic types(str, bytes) - except with wrap decorators
(initially) no pure python implementation of pydantic-core

The bad bits

What's changing in V2

Wrap Validators

class MyModel(BaseModel):
    appointment_time: datetime | None
    
    @validator('appointment_time', mode='wrap')
    def validate_appointment_time(cls, v, handler):
        if v == 'now':
            return datetime.now()
        
        try:
            return handler(v)
        except ValidationError:
            # we don't want to fail, so just use None
            return None

New Features

(Implemented, but not with this nice syntactic sugar)

AKA "The onion" - like middleware

Strict Mode

class StrictModel(BaseModel, strict=True):
    a_string: str
    an_int: int
    set_of_ints: Set[int]


class LaxModel(BaseModel):
    lax_string: str
    strict_string1: str = Field(..., strict=True)
    strict_string2: StrictStr

New Features

(Implemented, but not with this nice syntactic sugar)

(Does not apply when "downcasting" from JSON)

Smart Union

class MyModel(BaseModel):
    int_or_bool: int | bool
    bool_or_int: bool | int


print(MyModel(int_or_bool=1))  #> 1, 1
print(MyModel(int_or_bool=True))  #> True, True
print(MyModel(int_or_bool='1'))  #> 1, True :-( ?

New Features

(Implemented, but not with this nice syntactic sugar)

(Using strict mode)

(Also works with models and model instances)

Optional vs. Nullable

class MyModel1(BaseModel):
    none_allowed_required: str | None
    none_allowed_not_required: str | None = None

      
from typing_extentions import Required, NotRequired


class MyModel2(BaseModel):  # ... maybe, do you want this?
    none_allowed_required: Required[str]
    none_allowed_not_required: NotRequired[str]

New Features

(Implemented, but not with this nice syntactic sugar)

(I'm no longer scared of the word "optional")

Validation without a model

from pydantic import validate

validate(List[int], [1, 2, '3'])  #> [1, 2, 3]

validate(List[int], [1, 2, 3], strict=True)  #> [1, 2, 3]

validate(List[int], [1, 2, '3'], strict=True)
#> raises ValidationError

New Features

(Implemented, but not with this nice syntactic sugar)

No intermediate model required (unlike parse_obj in V1)

Parsing JSON directly

class MyModel(BaseModel):
    name: str
    age: int
    friends: List[int]
    settings: Dict[str, float]

MyModel.validate_json('{...}')

New Features

(Implemented, but not with this nice syntactic sugar)

No json.loads - just rust JSON parsing straight into validation

We could add support for other formats (e.g. yaml, toml) the only side affect would be bigger binaries
Not yet possible to get the line number :-(

Speed

New Features

Benchmark	Speed up
Simple model (str, int, List[int], Dict[str, float])	15.97x
Simple model - JSON	11.56x
A bool (single value)	3.46x
Recursive model, 50 deep	3.99x
list of typed dicts, length 100	12.14x
list of ints, length 1000	25.49x

And more...

New Features

hopefully 🤞 less "performance guilt"™
context kwarg to validator functions
input value is available in errors
cleaning up the namespace - so you can use fields like "json", "dict" and "fields" and more importantly, so we can add more methods in future, either:
- all methods will have a prefix, e.g. "my_.model_dict()", ".model_json()", ".model_schema()"
- or, a namespace object: ".m.dict()", ".m.json()"

pydantic-core internals

from pydantic_core import SchemaValidator
schema_validator = SchemaValidator({'type': 'bool'})
print(repr(schema_validator))

Python extensions in Rust

(This code actually runs now!)

Let's start simple

SchemaValidator(name="bool", validator=BoolValidator)

print(schema_validator.validate_python(True)) -> True
print(schema_validator.validate_python(1))    -> True
print(schema_validator.validate_json('true')) -> True

pydantic-core internals

from pydantic_core import SchemaValidator
# Equivalent to: Dict[str, Optional[int]]
schema_validator = SchemaValidator({
    'type': 'dict',
    'keys': {'type': 'str'},
    'values': {'type': 'optional', 'schema': {'type': 'int'}}
})

Python extensions in Rust

Let's get a bit more complicated

SchemaValidator(name="dict", validator=DictValidator {
    strict: false,
    key_validator: Some(StrValidator),
    value_validator: Some(
        OptionalValidator {validator: IntValidator},
    ),
    min_items: None,
    max_items: None,
    try_instance_as_dict: false,
})

pydantic-core internals

class MyModel(BaseModel):
    name: str
    age: int | None = 42
    settings: dict[str, float]
    friends: list[int | str]

Python extensions in Rust

And finally...

(you don't need to read all this)

SchemaValidator(name="MyCoreModel", 
  validator=ModelClassValidator {
    strict: false,
    class: Py(0x12fe7e7c0), (MyCoreModel)
    new_method: Py(0x00101054130), (MyCoreModel.__new__)
    validator: ModelValidator {
        name: "Model",
        fields: [
            ModelField {
                name: "name",
                default: None,
                validator: StrValidator,
            },
            ModelField {
                name: "age",
                default: 42,
                validator: OptionalValidator { 
                  validator: IntValidator 
                },
            },
            ModelField {
                name: "settings",
                default: None,
                validator: DictValidator { ... },
            },
            ModelField {
                name: "friends",
                default: None,
                validator: ListValidator {
                    strict: false,
                    item_validator: Some(
                        UnionValidator {
                            choices: [
                                IntValidator,
                                StrValidator,
                            ],
                        },
                    ),
                    min_items: None,
                    max_items: None,
                },
            },
        ],
        extra_behavior: Ignore,
        extra_validator: None,
    },
})

Many other people have said all this, there are many (much better) talks about it.

But for completeness, the good:

Speed is the biggest win
Provides a way to hook into existing great libraries written in rust - rtoml and watchfiles - when is someone going to do this for ASGI?
Rust's error handling makes easy to catch and deal with errors
Rust provides excellent primitives for threading
pyo3 is amazing, getting started is very easy

The bad:

Writing rust will always be slower than python
there's a big learning curve

The ugly:

Fighting the borrow checker is boring
There will be boilerplate, macros to avoid boilerplate can be even worse...

The obvious things

Writing Python extensions in Rust

"Performance guilt":
- With rust there's no penalty for recursion
- and no penalty for small functions
- so ... you can build more modular code without paying a performance penalty
- The theory is that someone can come along in 5 years time and add another type to pydantic-core, and:
  - The type checker and linter will stop them doing dumb things
  - There will be zero runtime penalty if you don't use it
  - Their change can be small since there's no performance penalty for calling out to existing code
Contributions ... ?
- Will there be fewer, will they be "better"? or worse?

The less obvious things

Writing Python extensions in Rust

#[derive(Debug, Clone)]
pub struct OptionalValidator {
    validator: Box<dyn Validator>,
}

impl OptionalValidator {
    pub const EXPECTED_TYPE: &'static str = "optional";
}

impl Validator for OptionalValidator {
    fn build(schema: &PyDict, config: Option<&PyDict>) -> PyResult<Box<dyn Validator>> {
        let schema: &PyAny = schema.get_as_req("schema")?;
        Ok(Box::new(Self {
            validator: build_validator(schema, config)?.0,
        }))
    }

    fn validate<'s, 'data>(
        &'s self,
        py: Python<'data>,
        input: &'data dyn Input,
        extra: &Extra,
    ) -> ValResult<'data, PyObject> {
        match input.is_none() {
            true => Ok(py.None()),
            false => self.validator.validate(py, input, extra),
        }
    }

    fn validate_strict<'s, 'data>(
        &'s self,
        py: Python<'data>,
        input: &'data dyn Input,
        extra: &Extra,
    ) -> ValResult<'data, PyObject> {
        match input.is_none() {
            true => Ok(py.None()),
            false => self.validator.validate_strict(py, input, extra),
        }
    }

    fn set_ref(&mut self, name: &str, validator_arc: &ValidatorArc) -> PyResult<()> {
        self.validator.set_ref(name, validator_arc)
    }

    validator_boilerplate!(Self::EXPECTED_TYPE);
}

Writing Python extensions in Rust, example... (please don't cry)

mod optional;

...
lots of code...
...

    validator_match!(
        type_,
        dict,
        config,
        ... all the other validators
        // unions
        self::union::UnionValidator,
        self::optional::OptionalValidator,
        ...
    )

Writing Python extensions in Rust, example...

using my validator

Questions?

Checkout: github.com/samuelcolvin/pydantic-core

And: github.com/samuelcolvin/pydantic

Follow me on twitter: @samuel_colvin

Copy of Pydantic V2

By tunecom

Copy of Pydantic V2

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