2. decorators

These modules contain several decorators that can prove useful when creating models. These decorators originate from dataloom.decorators, and at this stage, we are referring to them as “experimental.”

@initialize()

Let’s examine a model named Profile, which appears as follows:

class Profile(Model):
    __tablename__: Optional[TableColumn] = TableColumn(name="profiles")
    id = PrimaryKeyColumn(type="int", auto_increment=True)
    avatar = Column(type="text", nullable=False)
    userId = ForeignKeyColumn(
        User,
        maps_to="1-1",
        type="int",
        required=True,
        onDelete="CASCADE",
        onUpdate="CASCADE",
    )

This is simply a Python class that inherits from the top-level class Model. However, it lacks some useful dunder methods such as __init__ and __repr__. In standard Python, we can achieve this functionality by using dataclasses. For example, we can modify our class as follows:

from dataclasses import dataclass

@dataclass
class Profile(Model):
    # ....

However, this approach doesn’t function as expected in dataloom columns. Hence, we’ve devised these experimental decorators to handle the generation of essential dunder methods required for working with dataloom. If you prefer not to use decorators, you always have the option to manually create these dunder methods. Here’s an example:

class Profile(Model):
    # ...
    def __init__(self, id: int | None, avatar: str | None, userId: int | None) -> None:
        self.id = id
        self.avatar = avatar
        self.userId = userId

    def __repr__(self) -> str:
        return f"<{self.__class__.__name__}:id={self.id}>"

    @property
    def to_dict(self):
        return {"id": self.id, "avatar": self.avatar, "userId": self.userId}

However, by using the initialize decorator, this functionality will be automatically generated for you. Here’s all you need to do:

from dataloom.decorators import initialize

@initialize(repr=True, to_dict=True, init=True, repr_identifier="id")
class Profile(Model):
    # ...

Tip

👉 Tip: Dataloom has a clever way of skipping the TableColumn because it doesn’t matter in this case.

The initialize decorator takes the following arguments:

Argument	Description	Type	Default	Required
to_dict	Flag indicating whether to generate a to_dict method.	bool	False	No
init	Flag indicating whether to generate an __init__ method.	bool	True	No
repr	Flag indicating whether to generate a __repr__ method.	bool	False	No
repr_identifier	Identifier for the attribute used in the __repr__ method.	str or None	None	No

Tip

👍 Pro Tip: Note that this decorator function allows us to interact with our data from the database in an object-oriented way in Python. Below is an example illustrating this concept:

profile = mysql_loom.find_by_pk(Profile, pk=1, select=["avatar", "id"])
profile = Profile(**profile)
print(profile)  # ? = <Profile:id=1>
print(profile.avatar)  # ? hello.jpg