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I used this idea to extend / monkeypatch SQLModel to work with STRUCT and ARRAY from BigQuery: # Monkeypatch sqlmodel.main.get_sqlalchemy_type to work with BigQuery's STRUCT and ARRAY types:
from typing import Any, Callable, List
import sqlalchemy.sql
import sqlmodel.main
from sqlmodel.main import get_sqlachemy_type as original_get_sqlalchemy_type
from pydantic import BaseModel, ConstrainedStr
from pydantic.fields import ModelField
from sqlalchemy import String
from sqlalchemy_bigquery import STRUCT, ARRAY
from typing_extensions import TypeAlias
_GetSqlalchemyTypeProtocol: TypeAlias = Callable[[ModelField], Any]
def extended_get_sqlalchemy_type(field: ModelField) -> Any:
# Add support for STRUCT
if issubclass(field.type_, BaseModel):
return STRUCT(
*((f.name, extended_get_sqlalchemy_type(f)) for f in field.type_.__fields__.values())
)
# Add support for ARRAY
if issubclass(field.type_, List):
if field.sub_fields:
return ARRAY(extended_get_sqlalchemy_type(field.sub_fields[0]))
return ARRAY()
# Add support for ConstrainedStr
if issubclass(field.type_, ConstrainedStr):
length = field.type_.max_length
if length is not None:
return String(length=length)
return String()
return original_get_sqlalchemy_type(field)
def patch_sqlmodel() -> None:
sqlmodel.main.get_sqlachemy_type = extended_get_sqlalchemy_type
patch_sqlmodel()
Now submodels can be used in SQLModel models and they get converted to STRUCT. e.g.: class Service(SQLModel):
id: str = Field(description="The ID of the Google Cloud Platform service that offers the SKU.")
description: str = Field(description="The Google Cloud Platform service that offers the SKU.")
class ServiceTable(SQLModel, table=True):
service: Service = Field()
I generally try to avoid this kind of patching shenanigans but this has greatly simplified my models and completely gotten rid of the need for sa_column in my SQLModel models |
|
I suggest to have a map that can be imported to modify and add type mappings between SQLModel and SQLAlchemy this also could solve #800 I have done something similar but to add additional support for different types my use case is to support the different JSON types of pydantic. import sqlmodel.main
from pydantic.types import Json as PydanticJson
from pydantic import JsonValue as PydanticJsonValue
from sqlalchemy.types import JSON
# Exportable and modifiable
TYPE_ANNOTATION_MAP = {
PydanticJson: JSON,
PydanticJsonValue: JSON
}
# Monkey patch for sqlmodel to support JSON type
_gst = sqlmodel.main.get_sqlalchemy_type
def get_sqlalchemy_type(field: Any) -> Any:
try:
return _gst(field)
except Exception as exc:
type_ = None
if hasattr(field, 'type_'):
type_ = field.type_
elif field.annotation:
type_ = field.annotation
if (
get_origin(type_) is Union
and len(type_.__args__) == 2
and type(None) in type_.__args__
):
type_ = next(arg for arg in type_.__args__ if arg is not None)
if get_origin(type_) is Annotated:
type_ = type(type_.__metadata__[0])
if type_ in TYPE_ANNOTATION_MAP.keys():
return TYPE_ANNOTATION_MAP[type_]
raise exc
sqlmodel.main.get_sqlalchemy_type = get_sqlalchemy_typeThe functionality in the first comment of this issue can be added by accept Callables also with SQLAlchemy types, for example from sqlmodel.main import TYPE_ANNOTATION_MAP
TYPE_ANNOTATION_MAP.extend({
PydanticJson: JSON,
PydanticJsonValue: JSON,
ConstrainedStr: lambda f: String(length=f.type_.max_length)
})If you want I could do a PR with this. Cheers PS: I have to mention and thanks to ChatLPO that allows me to post this suggestions based on the work in the company. |
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First Check
Commit to Help
Example Code
Description
Problem:
Wanted Solution
Allow user to use customized get_column_from_field | get_sqlalchemy_type function to fit with their own requirements.
Add parameter to model config like sa_column_builder: Callable[[ModelField], Column] = get_column_from_field.
Function get_column_from_field would be better split by the following concerns, to be used as a part of customized sa_column_builder implementation:
Possible effects on other issues/PRs:
p.s-1
Conversion rule between Field/column value may become necessary, mainly to serialize field value to column value.
(e.g. Classes inheriting BaseModel cannot be stored directly into sqlalchemy.JSON because it is not JSON or dict. We avoid this by adding json_serializer to create_engine. Deserialize part has no problem because JSON str -> BaseModel will be done by pydantic validation for now (pydantic v1))
p.s-2
IMO using sqlmodel.sql.sqltypes.AutoString() in alembic revision file is not good from the sight of future migration constancy, and this is one of the reason I overridden get_sqlalchemy_type function.
Wanted Code
Alternatives
Operating System
Linux
Operating System Details
No response
SQLModel Version
0.0.8
Python Version
3.10.7
Additional Context
No response