Использование Pydantic сегодня стало нормой, и это правильно. Но иногда на ревью вижу, что используют его не всегда корректно.
Например, метод BaseModel.model_dump() по умолчанию не преобразует стандартные типы, такие как datetime, UUID или Decimal, в простой сериализуемый для JSON вид. Тогда пишут кастмоный сериализатор для этих типов чтобы функция json.dump() не падала с ошибкой.
import uuid
from datetime import datetime
from decimal import Decimal
from uuid import UUID
from pydantic import BaseModel
class MyModel(BaseModel):
id: UUID
date: datetime
value: Decimal
obj = MyModel(
id=uuid.uuid4(),
date=datetime.now(),
value='1.23'
)
print(obj.model_dump())
# не подходит для json.dump
# {
# 'id': UUID('4f8c1bc4-25fd-40cd-9dbe-2c73639b0dc1'),
# 'date': datetime.datetime(2025, 12, 12, 12, 12, 12, 111111),
# 'value': Decimal('1.23')
# }
# добавляем свой кастомный сериализатор
json.dumps(obj.model_dump(), cls=MySerializer)
# {
# 'id': '4f8c1bc4-25fd-40cd-9dbe-2c73639b0dc1',
# 'date': '2025-12-12T12:12:12.111111',
# 'value': '1.23'
# }
В данном случае класс MySerializer обрабатывает datetime, UUID и Decimal. Например так:
class MySerializer(json.JSONEncoder):
def default(self, o):
if isinstance(o, Decimal):
return str(o)
elif isinstance(o, datetime):
return o.isoformat()
elif isinstance(o, UUID):
return str(o)
return super().default(o)
Специально для тех, кто всё еще так делает - в этом нет необходимости!
Pydantic может это сделать сам, просто нужно добавить параметр mode="json".
json.dumps(obj.model_dump(mode="json"))
# {
# 'id': '4f8c1bc4-25fd-40cd-9dbe-2c73639b0dc1',
# 'date': '2012-12-12T12:12:12.111111',
# 'value': '1.23'
# }
#pydantic#libs
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