Merge branch 'main' of https://github.com/abhiaagarwal/notes

content/programming/languages/python/sqlalchemy/pydantic.md

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+---
+title: Pydantic models in SQLAlchemy
+tags:
+  - observations
+---
+Been using this snippet a lot recently in SQLAlchemy to have Pydantic models be de/serialized to JSON transparently for a nice abstraction.
+
+```python
+from sqlalchemy import DateTime, Dialect, func
+from sqlalchemy.types import JSON, TypeDecorator, TypeEngine
+from sqlalchemy.dialects.postgresql import JSONB
+from pydantic import BaseModel
+
+
+class PydanticModelType[T: BaseModel](TypeDecorator[T]):
+    cache_ok = True
+    impl = JSON()
+
+    def __init__(self, pydantic_type: type[T]) -> None:
+        self.pydantic_type = pydantic_type
+        super().__init__()
+
+    @override
+    def load_dialect_impl(self, dialect: Dialect) -> TypeEngine[T]:
+        # Use JSONB for PostgreSQL and JSON for other databases.
+        if dialect.name == "postgresql":
+            return dialect.type_descriptor(JSONB())
+
+        return dialect.type_descriptor(JSON())
+
+    @override
+    def process_bind_param(self, value: T | None, dialect: Dialect) -> dict[str, Any] | None:
+        if value is not None:
+            return value.model_dump()
+        return value
+
+    @override
+    def process_result_value(self, value: dict[str, Any] | None, dialect: Dialect) -> T | None:
+        if value is not None:
+            return self.pydantic_type.model_validate(value)
+        return value
+```
+
+If using python<3.12, the `T` can be replaced with `T = TypeVar("T", bound=BaseModel)` and it should also subclass `Generic[T]`. 
+
+Then, inside a subclass of `DeclarativeBase`, you can use with the modern `Mapped/mapped_column` syntax like:
+
+```python
+from sqlalchemy.orm import (
+    DeclarativeBase,
+    Mapped,
+    mapped_column,
+)
+from pydantic import BaseModel
+
+class BaseTable(DeclarativeBase):
+    pass
+
+class MyModel(BaseModel):
+    pass
+
+class MyTable(BaseTable):
+    __tablename__ = "my_table"
+    config: Mapped[MyModel] = mapped_column(PydanticModelType(MyModel))
+```
+
+Unfortunately, SQLAlchemy doesn't have a nice way to infer that `MyModel` should be passed to `PydanticModelType` in an easy way, so we have to repeat ourselves.
+
+Additionally, when instantiating your SQLAlchemy engine, you need to pass Pydantic's JSON serializer. Otherwise, it will use Python's default JSON serializer, causing it to freak out.
+
+```python
+from pydantic_core import to_json
+
+def _json_serializer(obj: Any) -> str:
+    return to_json(obj).decode("utf-8")
+
+engine = create_engine(..., json_serializer=_json_serializer)
+```
+
+Note that there isn't a really good way to do change detection via the SQLAlchemy Session API, so you should always manually track your changes and commit when needed.
+
+This package looks like an interesting way to solve the problem, but I have no clue if it works: [`pydantic-changedetect`](https://github.com/team23/pydantic-changedetect/tree/main).