Python中的Pydantic序列化详解
Pydantic系列之序列化
model_dump
model_dump将对象转化为字典对象,之后便可以调用Python标准库序列化为json字符串,会序列化嵌套对象。
也可以使用dict(model)将对象转化为字典,但嵌套对象不会被转化为字典。
自定义序列化
@field_serializer
装饰在实例方法或者静态方法,被装饰方法可以是以下四种。
- (self, value: Any, info: FieldSerializationInfo)
- (self, value: Any, nxt: SerializerFunctionWrapHandler, info: FieldSerializationInfo)
- (value: Any, info: SerializationInfo)
- (value: Any, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)
默认为PlainSerializer,不走pydantic的序列化逻辑,此时的方法签名只能是1或3,
nxt参数为pydantic序列化链
mode='wrap’支持上述四个方法签名,可完成前置处理,pydantic序列化逻辑,载返回之前再处理的逻辑。
from datetime import datetime, timedelta, timezone from pydantic import BaseModel, ConfigDict, field_serializer from pydantic_core.core_schema import FieldSerializationInfo, SerializerFunctionWrapHandler class WithCustomEncoders(BaseModel): model_config = ConfigDict(ser_json_timedelta='iso8601') dt: datetime diff: timedelta diff2: timedelta @field_serializer('dt') def serialize_dt(self, dt: datetime, _info: FieldSerializationInfo): print(_info) return dt.timestamp() # 下面的装饰器先执行 @field_serializer('diff') def ssse(self, diff: timedelta, info: FieldSerializationInfo): print(info) return diff.total_seconds() @field_serializer('diff2', mode='wrap') @staticmethod def diff2_ser(diff2: timedelta, nxt: SerializerFunctionWrapHandler, info: FieldSerializationInfo): value = nxt(diff2) return value + 'postprocess' m = WithCustomEncoders( dt=datetime(2032, 6, 1, tzinfo=timezone.utc), diff=timedelta(minutes=2), diff2=timedelta(minutes=1) ) print(m.model_dump_json()) # {"dt":1969660800.0,"diff":120.0,"diff2":"PT60Spostprocess"}
@model_serializer
- (self, info: FieldSerializationInfo),mode=‘plain’
- (self, nxt: SerializerFunctionWrapHandler, info: FieldSerializationInfo),mode=‘plain’
from typing import Dict, Any from pydantic import BaseModel, model_serializer from pydantic_core.core_schema import SerializerFunctionWrapHandler, SerializationInfo class Model(BaseModel): x: str @model_serializer def ser_model(self, info: SerializationInfo): print(info) return {'x': f'xxxxxx {self.x}'} @model_serializer(mode='wrap') def ser_model_wrap(self, nxt: SerializerFunctionWrapHandler, info: SerializationInfo) -> Dict[str, Any]: print(info) return {'x': f'serialized {nxt(self)}'} print(Model(x='test value').model_dump_json()) # {"x":"serialized {'x': 'test value'}"}
PlainSerializer和WrapSerializer
from typing import Any from typing_extensions import Annotated from pydantic import BaseModel, SerializerFunctionWrapHandler from pydantic.functional_serializers import WrapSerializer, PlainSerializer def ser_wrap(v: Any, nxt: SerializerFunctionWrapHandler) -> str: return f'{nxt(v + 1):,}' FancyInt = Annotated[int, WrapSerializer(ser_wrap, when_used='json')] DoubleInt = Annotated[int, PlainSerializer(lambda x: x * 2)] class MyModel(BaseModel): x: FancyInt y: DoubleInt print(MyModel(x=1234, y=2).model_dump()) # {'x': 1234, 'y': 4} print(MyModel(x=1234, y=2).model_dump(mode='json')) # {'x': '1,235', 'y': 4}
如何指定某个类型的序列化行为
在 pydantic v1 版本,configdict有个json_encoders参数,可以配置指定类型的序列化行为。 在 pydantic v2 版本,不推荐json_encoders参数,可使用如下方式
def serialize_datetime(value: datetime.datetime, __: SerializerFunctionWrapHandler, _: SerializationInfo): return value.strftime('%Y-%m-%d %H:%M:%S') LocalDateTime = Annotated[datetime.datetime, WrapSerializer(serialize_datetime, when_used='json')]
按照声明类型序列化,而不是实际类型
当某个属性的声明类型是可序列化类型时,如 BaseModel , dataclass , TypedDict 等,按照声明类型序列化,而不是实际类型。如果想改变这种行为,可以使用 SerializeAsAny 。
from pydantic import BaseModel, SerializeAsAny class User(BaseModel): name: str class UserLogin(User): password: str class OuterModel(BaseModel): # 声明为User类型,按照User类序列化,只有name字段 user: User user1: SerializeAsAny[User] = UserLogin(name='serialize as any', password='hunter') # 实际类型为UserLogin user = UserLogin(name='pydantic', password='hunter2') m = OuterModel(user=user) print(m) # user=UserLogin(name='pydantic', password='hunter2') user1=UserLogin(name='serialize as any', password='hunter') print(m.model_dump()) # {'user': {'name': 'pydantic'}, 'user1': {'name': 'serialize as any', 'password': 'hunter'}}
pickle
# TODO need to get pickling to work import pickle from pydantic import BaseModel class FooBarModel(BaseModel): a: str b: int m = FooBarModel(a='hello', b=123) print(m) #> a='hello' b=123 data = pickle.dumps(m) print(data[:20]) #> b'\x80\x04\x95\x95\x00\x00\x00\x00\x00\x00\x00\x8c\x08__main_' m2 = pickle.loads(data) print(m2) #> a='hello' b=123
灵活的exclude和include
- exclude,include支持集合,字典
- 支持集合指定位置序列化或不序列化, exclude = {'items' :{0: True, -1: False} , include = {'items': {'__all__':{'id':False}}}
from pydantic import BaseModel, SecretStr class User(BaseModel): id: int username: str password: SecretStr class Transaction(BaseModel): id: str user: User value: int t = Transaction( id='1234567890', user=User(id=42, username='JohnDoe', password='hashedpassword'), value=9876543210, ) # using a set: print(t.model_dump(exclude={'user', 'value'})) #> {'id': '1234567890'} # using a dict: print(t.model_dump(exclude={'user': {'username', 'password'}, 'value': True})) #> {'id': '1234567890', 'user': {'id': 42}} print(t.model_dump(include={'id': True, 'user': {'id'}})) #> {'id': '1234567890', 'user': {'id': 42}}
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