Performance tips¶
In most cases Pydantic won't be your bottle neck, only follow this if you're sure it's necessary.
Use model_validate_json() not model_validate(json.loads(...))¶
On model_validate(json.loads(...)), the JSON is parsed in Python, then converted to a dict, then it's validated internally.
On the other hand, model_validate_json() already performs the validation internally.
TypeAdapter instantiated once¶
The idea here is to avoid constructing validators and serializers more than necessary. Each time a TypeAdapter is instantiated,
it will construct a new validator and serializer. If you're using a TypeAdapter in a function, it will be instantiated each time
the function is called. Instead, instantiate it once, and reuse it.
from typing import List
from pydantic import TypeAdapter
def my_func():
adapter = TypeAdapter(List[int])
# do something with adapter
from typing import List
from pydantic import TypeAdapter
adapter = TypeAdapter(List[int])
def my_func():
...
# do something with adapter
Sequence vs list or tuple - Mapping vs dict¶
When using Sequence, Pydantic calls isinstance(value, Sequence) to check if the value is a sequence.
Also, Pydantic will try to validate against different types of sequences, like list and tuple.
If you know the value is a list or tuple, use list or tuple instead of Sequence.
The same applies to Mapping and dict.
If you know the value is a dict, use dict instead of Mapping.
Don't do validation when you don't have to - use Any to keep the value unchanged¶
If you don't need to validate a value, use Any to keep the value unchanged.
from typing import Any
from pydantic import BaseModel
class Model(BaseModel):
a: Any
model = Model(a=1)
Avoid extra information via subclasses of primitives¶
class CompletedStr(str):
def __init__(self, s: str):
self.s = s
self.done = False
from pydantic import BaseModel
class CompletedModel(BaseModel):
s: str
done: bool = False
===
Use tagged union, not union¶
Tagged union (or discriminated union) is a union with a field that indicates which type it is.
from typing import Any
from typing_extensions import Literal
from pydantic import BaseModel, Field
class DivModel(BaseModel):
el_type: Literal['div'] = 'div'
class_name: str | None = None
children: list[Any] | None = None
class SpanModel(BaseModel):
el_type: Literal['span'] = 'span'
class_name: str | None = None
contents: str | None = None
class ButtonModel(BaseModel):
el_type: Literal['button'] = 'button'
class_name: str | None = None
contents: str | None = None
class InputModel(BaseModel):
el_type: Literal['input'] = 'input'
class_name: str | None = None
value: str | None = None
class Html(BaseModel):
contents: DivModel | SpanModel | ButtonModel | InputModel = Field(
discriminator='el_type'
)
See Discriminated Unions for more details.
Use Literal not Enum¶
Instead of using Enum, use Literal to define the structure of the data.
Performance comparison
With a simple benchmark, Literal is about ~3x faster than Enum:
import enum
from timeit import timeit
from typing_extensions import Literal
from pydantic import TypeAdapter
ta = TypeAdapter(Literal['a', 'b'])
result1 = timeit(lambda: ta.validate_python('a'), number=10000)
class AB(str, enum.Enum):
a = 'a'
b = 'b'
ta = TypeAdapter(AB)
result2 = timeit(lambda: ta.validate_python('a'), number=10000)
print(result2 / result1)
Use TypedDict over nested models¶
Instead of using nested models, use TypedDict to define the structure of the data.
Performance comparison
With a simple benchmark, TypedDict is about ~2.5x faster than nested models:
from timeit import timeit
from typing_extensions import TypedDict
from pydantic import BaseModel, TypeAdapter
class A(TypedDict):
a: str
b: int
class TypedModel(TypedDict):
a: A
class B(BaseModel):
a: str
b: int
class Model(BaseModel):
b: B
ta = TypeAdapter(TypedModel)
result1 = timeit(
lambda: ta.validate_python({'a': {'a': 'a', 'b': 2}}), number=10000
)
result2 = timeit(
lambda: Model.model_validate({'b': {'a': 'a', 'b': 2}}), number=10000
)
print(result2 / result1)