Release: 2.0.45 | Release Date: December 9, 2025

# SQLAlchemy 2.0 Documentation

# Integration with dataclasses and attrs

SQLAlchemy as of version 2.0 features “native dataclass” integration where an Annotated Declarative Table mapping may be turned into a Python dataclass by adding a single mixin or decorator to mapped classes.

Added in version 2.0: Integrated dataclass creation with ORM Declarative classes

There are also patterns available that allow existing dataclasses to be mapped, as well as to map classes instrumented by the attrs third party integration library.

## Declarative Dataclass Mapping

SQLAlchemy Annotated Declarative Table mappings may be augmented with an additional mixin class or decorator directive, which will add an additional step to the Declarative process after the mapping is complete that will convert the mapped class in-place into a Python dataclass, before completing the mapping process which applies ORM-specific instrumentation to the class. The most prominent behavioral addition this provides is generation of an `__init__()` method with fine-grained control over positional and keyword arguments with or without defaults, as well as generation of methods like `__repr__()` and `__eq__()`.

From a PEP 484 typing perspective, the class is recognized as having Dataclass-specific behaviors, most notably by taking advantage of PEP 681 “Dataclass Transforms”, which allows typing tools to consider the class as though it were explicitly decorated using the `@dataclasses.dataclass` decorator.

Note

Support for PEP 681 in typing tools as of April 4, 2023 is limited and is currently known to be supported by Pyright as well as Mypy as of version 1.2. Note that Mypy 1.1.1 introduced PEP 681 support but did not correctly accommodate Python descriptors which will lead to errors when using SQLAlchemy’s ORM mapping scheme.

See also

https://peps.python.org/pep-0681/#the-dataclass-transform-decorator - background on how libraries like SQLAlchemy enable PEP 681 support

Dataclass conversion may be added to any Declarative class either by adding the `MappedAsDataclass` mixin to a `DeclarativeBase` class hierarchy, or for decorator mapping by using the `registry.mapped_as_dataclass()` class decorator.

The `MappedAsDataclass` mixin may be applied either to the Declarative `Base` class or any superclass, as in the example below:

    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import MappedAsDataclass

    class Base(MappedAsDataclass, DeclarativeBase):
        """subclasses will be converted to dataclasses"""

    class User(Base):
        __tablename__ = "user_account"

        id: Mapped[int] = mapped_column(init=False, primary_key=True)
        name: Mapped[str]

Or may be applied directly to classes that extend from the Declarative base:

    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import MappedAsDataclass

    class Base(DeclarativeBase):
        pass

    class User(MappedAsDataclass, Base):
        """User class will be converted to a dataclass"""

        __tablename__ = "user_account"

        id: Mapped[int] = mapped_column(init=False, primary_key=True)
        name: Mapped[str]

When using the decorator form, the `registry.mapped_as_dataclass()` decorator is supported:

    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import registry

    reg = registry()

    @reg.mapped_as_dataclass
    class User:
        __tablename__ = "user_account"

        id: Mapped[int] = mapped_column(init=False, primary_key=True)
        name: Mapped[str]

The same method is available in a standalone function form, which may have better compatibility with some versions of the mypy type checker:

    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_as_dataclass
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import registry

    reg = registry()

    @mapped_as_dataclass(reg)
    class User:
        __tablename__ = "user_account"

        id: Mapped[int] = mapped_column(init=False, primary_key=True)
        name: Mapped[str]

Added in version 2.0.44: Added `mapped_as_dataclass()` after observing mypy compatibility issues with the method form of the same feature

### Class level feature configuration

Support for dataclasses features is partial. Currently supported are the `init`, `repr`, `eq`, `order` and `unsafe_hash` features, `match_args` and `kw_only` are supported on Python 3.10+. Currently not supported are the `frozen` and `slots` features.

When using the mixin class form with `MappedAsDataclass`, class configuration arguments are passed as class-level parameters:

    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import MappedAsDataclass

    class Base(DeclarativeBase):
        pass

    class User(MappedAsDataclass, Base, repr=False, unsafe_hash=True):
        """User class will be converted to a dataclass"""

        __tablename__ = "user_account"

        id: Mapped[int] = mapped_column(init=False, primary_key=True)
        name: Mapped[str]

When using the decorator form with `registry.mapped_as_dataclass()` or `mapped_as_dataclass()`, class configuration arguments are passed to the decorator directly:

    from sqlalchemy.orm import registry
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column

    reg = registry()

    @reg.mapped_as_dataclass(unsafe_hash=True)
    class User:
        """User class will be converted to a dataclass"""

        __tablename__ = "user_account"

        id: Mapped[int] = mapped_column(init=False, primary_key=True)
        name: Mapped[str]

For background on dataclass class options, see the dataclasses documentation at @dataclasses.dataclass.

### Attribute Configuration

SQLAlchemy native dataclasses differ from normal dataclasses in that attributes to be mapped are described using the `Mapped` generic annotation container in all cases. Mappings follow the same forms as those documented at Declarative Table with mapped_column(), and all features of `mapped_column()` and `Mapped` are supported.

Additionally, ORM attribute configuration constructs including `mapped_column()`, `relationship()` and `composite()` support per-attribute field options, including `init`, `default`, `default_factory` and `repr`. The names of these arguments is fixed as specified in PEP 681. Functionality is equivalent to dataclasses:

- `init`, as in `mapped_column.init`, `relationship.init`, if False indicates the field should not be part of the `__init__()` method

- `default`, as in `mapped_column.default`, `relationship.default` indicates a default value for the field as given as a keyword argument in the `__init__()` method.

- `default_factory`, as in `mapped_column.default_factory`, `relationship.default_factory`, indicates a callable function that will be invoked to generate a new default value for a parameter if not passed explicitly to the `__init__()` method.

- `repr` True by default, indicates the field should be part of the generated `__repr__()` method

Another key difference from dataclasses is that default values for attributes must be configured using the `default` parameter of the ORM construct, such as `mapped_column(default=None)`. A syntax that resembles dataclass syntax which accepts simple Python values as defaults without using `@dataclases.field()` is not supported.

As an example using `mapped_column()`, the mapping below will produce an `__init__()` method that accepts only the fields `name` and `fullname`, where `name` is required and may be passed positionally, and `fullname` is optional. The `id` field, which we expect to be database-generated, is not part of the constructor at all:

    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import registry

    reg = registry()

    @reg.mapped_as_dataclass
    class User:
        __tablename__ = "user_account"

        id: Mapped[int] = mapped_column(init=False, primary_key=True)
        name: Mapped[str]
        fullname: Mapped[str] = mapped_column(default=None)

    # 'fullname' is optional keyword argument
    u1 = User("name")

#### Column Defaults

In order to accommodate the name overlap of the `default` argument with the existing `Column.default` parameter of the `Column` construct, the `mapped_column()` construct disambiguates the two names by adding a new parameter `mapped_column.insert_default`, which will be populated directly into the `Column.default` parameter of `Column`, independently of what may be set on `mapped_column.default`, which is always used for the dataclasses configuration. For example, to configure a datetime column with a `Column.default` set to the `func.utc_timestamp()` SQL function, but where the parameter is optional in the constructor:

    from datetime import datetime

    from sqlalchemy import func
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import registry

    reg = registry()

    @reg.mapped_as_dataclass
    class User:
        __tablename__ = "user_account"

        id: Mapped[int] = mapped_column(init=False, primary_key=True)
        created_at: Mapped[datetime] = mapped_column(
            insert_default=func.utc_timestamp(), default=None
        )

With the above mapping, an `INSERT` for a new `User` object where no parameter for `created_at` were passed proceeds as:

    >>> with Session(e) as session:
    ...     session.add(User())
    ...     session.commit()
    BEGIN (implicit)
    INSERT INTO user_account (created_at) VALUES (utc_timestamp())
    [generated in 0.00010s] ()
    COMMIT

#### Integration with Annotated

The approach introduced at Mapping Whole Column Declarations to Python Types illustrates how to use PEP 593 `Annotated` objects to package whole `mapped_column()` constructs for re-use. While `Annotated` objects can be combined with the use of dataclasses, dataclass-specific keyword arguments unfortunately cannot be used within the Annotated construct. This includes PEP 681-specific arguments `init`, `default`, `repr`, and `default_factory`, which must be present in a `mapped_column()` or similar construct inline with the class attribute.

Changed in version 2.0.14/2.0.22: the `Annotated` construct when used with an ORM construct like `mapped_column()` cannot accommodate dataclass field parameters such as `init` and `repr` - this use goes against the design of Python dataclasses and is not supported by PEP 681, and therefore is also rejected by the SQLAlchemy ORM at runtime. A deprecation warning is now emitted and the attribute will be ignored.

As an example, the `init=False` parameter below will be ignored and additionally emit a deprecation warning:

    from typing import Annotated

    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import registry

    # typing tools as well as SQLAlchemy will ignore init=False here
    intpk = Annotated[int, mapped_column(init=False, primary_key=True)]

    reg = registry()

    @reg.mapped_as_dataclass
    class User:
        __tablename__ = "user_account"
        id: Mapped[intpk]

    # typing error as well as runtime error: Argument missing for parameter "id"
    u1 = User()

Instead, `mapped_column()` must be present on the right side as well with an explicit setting for `mapped_column.init`; the other arguments can remain within the `Annotated` construct:

    from typing import Annotated

    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import registry

    intpk = Annotated[int, mapped_column(primary_key=True)]

    reg = registry()

    @reg.mapped_as_dataclass
    class User:
        __tablename__ = "user_account"

        # init=False and other pep-681 arguments must be inline
        id: Mapped[intpk] = mapped_column(init=False)

    u1 = User()

### Using mixins and abstract superclasses

Any mixins or base classes that are used in a `MappedAsDataclass` mapped class which include `Mapped` attributes must themselves be part of a `MappedAsDataclass` hierarchy, such as in the example below using a mixin:

    class Mixin(MappedAsDataclass):
        create_user: Mapped[int] = mapped_column()
        update_user: Mapped[Optional[int]] = mapped_column(default=None, init=False)

    class Base(DeclarativeBase, MappedAsDataclass):
        pass

    class User(Base, Mixin):
        __tablename__ = "sys_user"

        uid: Mapped[str] = mapped_column(
            String(50), init=False, default_factory=uuid4, primary_key=True
        )
        username: Mapped[str] = mapped_column()
        email: Mapped[str] = mapped_column()

Python type checkers which support PEP 681 will otherwise not consider attributes from non-dataclass mixins to be part of the dataclass.

Deprecated since version 2.0.8: Using mixins and abstract bases within `MappedAsDataclass` or `registry.mapped_as_dataclass()` hierarchies which are not themselves dataclasses is deprecated, as these fields are not supported by PEP 681 as belonging to the dataclass. A warning is emitted for this case which will later be an error.

See also

When transforming <cls> to a dataclass, attribute(s) originate from superclass <cls> which is not a dataclass. - background on rationale

### Relationship Configuration

The `Mapped` annotation in combination with `relationship()` is used in the same way as described at Basic Relationship Patterns. When specifying a collection-based `relationship()` as an optional keyword argument, the `relationship.default_factory` parameter must be passed and it must refer to the collection class that’s to be used. Many-to-one and scalar object references may make use of `relationship.default` if the default value is to be `None`:

    from typing import List

    from sqlalchemy import ForeignKey
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import registry
    from sqlalchemy.orm import relationship

    reg = registry()

    @reg.mapped_as_dataclass
    class Parent:
        __tablename__ = "parent"
        id: Mapped[int] = mapped_column(primary_key=True)
        children: Mapped[List["Child"]] = relationship(
            default_factory=list, back_populates="parent"
        )

    @reg.mapped_as_dataclass
    class Child:
        __tablename__ = "child"
        id: Mapped[int] = mapped_column(primary_key=True)
        parent_id: Mapped[int] = mapped_column(ForeignKey("parent.id"))
        parent: Mapped["Parent"] = relationship(default=None)

The above mapping will generate an empty list for `Parent.children` when a new `Parent()` object is constructed without passing `children`, and similarly a `None` value for `Child.parent` when a new `Child()` object is constructed without passing `parent`.

While the `relationship.default_factory` can be automatically derived from the given collection class of the `relationship()` itself, this would break compatibility with dataclasses, as the presence of `relationship.default_factory` or `relationship.default` is what determines if the parameter is to be required or optional when rendered into the `__init__()` method.

### Using Non-Mapped Dataclass Fields

When using Declarative dataclasses, non-mapped fields may be used on the class as well, which will be part of the dataclass construction process but will not be mapped. Any field that does not use `Mapped` will be ignored by the mapping process. In the example below, the fields `ctrl_one` and `ctrl_two` will be part of the instance-level state of the object, but will not be persisted by the ORM:

    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import registry

    reg = registry()

    @reg.mapped_as_dataclass
    class Data:
        __tablename__ = "data"

        id: Mapped[int] = mapped_column(init=False, primary_key=True)
        status: Mapped[str]

        ctrl_one: Optional[str] = None
        ctrl_two: Optional[str] = None

Instance of `Data` above can be created as:

    d1 = Data(status="s1", ctrl_one="ctrl1", ctrl_two="ctrl2")

A more real world example might be to make use of the Dataclasses `InitVar` feature in conjunction with the `__post_init__()` feature to receive init-only fields that can be used to compose persisted data. In the example below, the `User` class is declared using `id`, `name` and `password_hash` as mapped features, but makes use of init-only `password` and `repeat_password` fields to represent the user creation process (note: to run this example, replace the function `your_crypt_function_here()` with a third party crypt function, such as bcrypt or argon2-cffi):

    from dataclasses import InitVar
    from typing import Optional

    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import registry

    reg = registry()

    @reg.mapped_as_dataclass
    class User:
        __tablename__ = "user_account"

        id: Mapped[int] = mapped_column(init=False, primary_key=True)
        name: Mapped[str]

        password: InitVar[str]
        repeat_password: InitVar[str]

        password_hash: Mapped[str] = mapped_column(init=False, nullable=False)

        def __post_init__(self, password: str, repeat_password: str):
            if password != repeat_password:
                raise ValueError("passwords do not match")

            self.password_hash = your_crypt_function_here(password)

The above object is created with parameters `password` and `repeat_password`, which are consumed up front so that the `password_hash` variable may be generated:

    >>> u1 = User(name="some_user", password="xyz", repeat_password="xyz")
    >>> u1.password_hash
    '$6$9ppc... (example crypted string....)'

Changed in version 2.0.0rc1: When using `registry.mapped_as_dataclass()` or `MappedAsDataclass`, fields that do not include the `Mapped` annotation may be included, which will be treated as part of the resulting dataclass but not be mapped, without the need to also indicate the `__allow_unmapped__` class attribute. Previous 2.0 beta releases would require this attribute to be explicitly present, even though the purpose of this attribute was only to allow legacy ORM typed mappings to continue to function.

### Integrating with Alternate Dataclass Providers such as Pydantic

Warning

The dataclass layer of Pydantic is not fully compatible with SQLAlchemy’s class instrumentation without additional internal changes, and many features such as related collections may not work correctly.

For Pydantic compatibility, please consider the SQLModel ORM which is built with Pydantic on top of SQLAlchemy ORM, which includes special implementation details which explicitly resolve these incompatibilities.

SQLAlchemy’s `MappedAsDataclass` class and `registry.mapped_as_dataclass()` method call directly into the Python standard library `dataclasses.dataclass` class decorator, after the declarative mapping process has been applied to the class. This function call may be swapped out for alternateive dataclasses providers, such as that of Pydantic, using the `dataclass_callable` parameter accepted by `MappedAsDataclass` as a class keyword argument as well as by `registry.mapped_as_dataclass()`:

    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import MappedAsDataclass
    from sqlalchemy.orm import registry

    class Base(
        MappedAsDataclass,
        DeclarativeBase,
        dataclass_callable=pydantic.dataclasses.dataclass,
    ):
        pass

    class User(Base):
        __tablename__ = "user"

        id: Mapped[int] = mapped_column(primary_key=True)
        name: Mapped[str]

The above `User` class will be applied as a dataclass, using Pydantic’s `pydantic.dataclasses.dataclasses` callable. The process is available both for mapped classes as well as mixins that extend from `MappedAsDataclass` or which have `registry.mapped_as_dataclass()` applied directly.

Added in version 2.0.4: Added the `dataclass_callable` class and method parameters for `MappedAsDataclass` and `registry.mapped_as_dataclass()`, and adjusted some of the dataclass internals to accommodate more strict dataclass functions such as that of Pydantic.

## Applying ORM Mappings to an existing dataclass (legacy dataclass use)

Legacy Feature

The approaches described here are superseded by the Declarative Dataclass Mapping feature new in the 2.0 series of SQLAlchemy. This newer version of the feature builds upon the dataclass support first added in version 1.4, which is described in this section.

To map an existing dataclass, SQLAlchemy’s “inline” declarative directives cannot be used directly; ORM directives are assigned using one of three techniques:

- Using “Declarative with Imperative Table”, the table / column to be mapped is defined using a `Table` object assigned to the `__table__` attribute of the class; relationships are defined within `__mapper_args__` dictionary. The class is mapped using the `registry.mapped()` decorator. An example is below at Mapping pre-existing dataclasses using Declarative With Imperative Table.

- Using full “Declarative”, the Declarative-interpreted directives such as `Column`, `relationship()` are added to the `.metadata` dictionary of the `dataclasses.field()` construct, where they are consumed by the declarative process. The class is again mapped using the `registry.mapped()` decorator. See the example below at Mapping pre-existing dataclasses using Declarative-style fields.

- An “Imperative” mapping can be applied to an existing dataclass using the `registry.map_imperatively()` method to produce the mapping in exactly the same way as described at Imperative Mapping. This is illustrated below at Mapping pre-existing dataclasses using Imperative Mapping.

The general process by which SQLAlchemy applies mappings to a dataclass is the same as that of an ordinary class, but also includes that SQLAlchemy will detect class-level attributes that were part of the dataclasses declaration process and replace them at runtime with the usual SQLAlchemy ORM mapped attributes. The `__init__` method that would have been generated by dataclasses is left intact, as is the same for all the other methods that dataclasses generates such as `__eq__()`, `__repr__()`, etc.

### Mapping pre-existing dataclasses using Declarative With Imperative Table

An example of a mapping using `@dataclass` using Declarative with Imperative Table (a.k.a. Hybrid Declarative) is below. A complete `Table` object is constructed explicitly and assigned to the `__table__` attribute. Instance fields are defined using normal dataclass syntaxes. Additional `MapperProperty` definitions such as `relationship()`, are placed in the __mapper_args__ class-level dictionary underneath the `properties` key, corresponding to the `Mapper.properties` parameter:

    from __future__ import annotations

    from dataclasses import dataclass, field
    from typing import List, Optional

    from sqlalchemy import Column, ForeignKey, Integer, String, Table
    from sqlalchemy.orm import registry, relationship

    mapper_registry = registry()

    @mapper_registry.mapped
    @dataclass
    class User:
        __table__ = Table(
            "user",
            mapper_registry.metadata,
            Column("id", Integer, primary_key=True),
            Column("name", String(50)),
            Column("fullname", String(50)),
            Column("nickname", String(12)),
        )
        id: int = field(init=False)
        name: Optional[str] = None
        fullname: Optional[str] = None
        nickname: Optional[str] = None
        addresses: List[Address] = field(default_factory=list)

        __mapper_args__ = {  # type: ignore
            "properties": {
                "addresses": relationship("Address"),
            }
        }

    @mapper_registry.mapped
    @dataclass
    class Address:
        __table__ = Table(
            "address",
            mapper_registry.metadata,
            Column("id", Integer, primary_key=True),
            Column("user_id", Integer, ForeignKey("user.id")),
            Column("email_address", String(50)),
        )
        id: int = field(init=False)
        user_id: int = field(init=False)
        email_address: Optional[str] = None

In the above example, the `User.id`, `Address.id`, and `Address.user_id` attributes are defined as `field(init=False)`. This means that parameters for these won’t be added to `__init__()` methods, but `Session` will still be able to set them after getting their values during flush from autoincrement or other default value generator. To allow them to be specified in the constructor explicitly, they would instead be given a default value of `None`.

For a `relationship()` to be declared separately, it needs to be specified directly within the `Mapper.properties` dictionary which itself is specified within the `__mapper_args__` dictionary, so that it is passed to the constructor for `Mapper`. An alternative to this approach is in the next example.

Warning

Declaring a dataclass `field()` setting a `default` together with `init=False` will not work as would be expected with a totally plain dataclass, since the SQLAlchemy class instrumentation will replace the default value set on the class by the dataclass creation process. Use `default_factory` instead. This adaptation is done automatically when making use of Declarative Dataclass Mapping.

### Mapping pre-existing dataclasses using Declarative-style fields

Legacy Feature

This approach to Declarative mapping with dataclasses should be considered as legacy. It will remain supported however is unlikely to offer any advantages against the new approach detailed at Declarative Dataclass Mapping.

Note that mapped_column() is not supported with this use; the `Column` construct should continue to be used to declare table metadata within the `metadata` field of `dataclasses.field()`.

The fully declarative approach requires that `Column` objects are declared as class attributes, which when using dataclasses would conflict with the dataclass-level attributes. An approach to combine these together is to make use of the `metadata` attribute on the `dataclass.field` object, where SQLAlchemy-specific mapping information may be supplied. Declarative supports extraction of these parameters when the class specifies the attribute `__sa_dataclass_metadata_key__`. This also provides a more succinct method of indicating the `relationship()` association:

    from __future__ import annotations

    from dataclasses import dataclass, field
    from typing import List

    from sqlalchemy import Column, ForeignKey, Integer, String
    from sqlalchemy.orm import registry, relationship

    mapper_registry = registry()

    @mapper_registry.mapped
    @dataclass
    class User:
        __tablename__ = "user"

        __sa_dataclass_metadata_key__ = "sa"
        id: int = field(init=False, metadata={"sa": Column(Integer, primary_key=True)})
        name: str = field(default=None, metadata={"sa": Column(String(50))})
        fullname: str = field(default=None, metadata={"sa": Column(String(50))})
        nickname: str = field(default=None, metadata={"sa": Column(String(12))})
        addresses: List[Address] = field(
            default_factory=list, metadata={"sa": relationship("Address")}
        )

    @mapper_registry.mapped
    @dataclass
    class Address:
        __tablename__ = "address"
        __sa_dataclass_metadata_key__ = "sa"
        id: int = field(init=False, metadata={"sa": Column(Integer, primary_key=True)})
        user_id: int = field(init=False, metadata={"sa": Column(ForeignKey("user.id"))})
        email_address: str = field(default=None, metadata={"sa": Column(String(50))})

#### Using Declarative Mixins with pre-existing dataclasses

In the section Composing Mapped Hierarchies with Mixins, Declarative Mixin classes are introduced. One requirement of declarative mixins is that certain constructs that can’t be easily duplicated must be given as callables, using the `declared_attr` decorator, such as in the example at Mixing in Relationships:

    class RefTargetMixin:
        @declared_attr
        def target_id(cls) -> Mapped[int]:
            return mapped_column("target_id", ForeignKey("target.id"))

        @declared_attr
        def target(cls):
            return relationship("Target")

This form is supported within the Dataclasses `field()` object by using a lambda to indicate the SQLAlchemy construct inside the `field()`. Using `declared_attr()` to surround the lambda is optional. If we wanted to produce our `User` class above where the ORM fields came from a mixin that is itself a dataclass, the form would be:

    @dataclass
    class UserMixin:
        __tablename__ = "user"

        __sa_dataclass_metadata_key__ = "sa"

        id: int = field(init=False, metadata={"sa": Column(Integer, primary_key=True)})

        addresses: List[Address] = field(
            default_factory=list, metadata={"sa": lambda: relationship("Address")}
        )

    @dataclass
    class AddressMixin:
        __tablename__ = "address"
        __sa_dataclass_metadata_key__ = "sa"
        id: int = field(init=False, metadata={"sa": Column(Integer, primary_key=True)})
        user_id: int = field(
            init=False, metadata={"sa": lambda: Column(ForeignKey("user.id"))}
        )
        email_address: str = field(default=None, metadata={"sa": Column(String(50))})

    @mapper_registry.mapped
    class User(UserMixin):
        pass

    @mapper_registry.mapped
    class Address(AddressMixin):
        pass

Added in version 1.4.2: Added support for “declared attr” style mixin attributes, namely `relationship()` constructs as well as `Column` objects with foreign key declarations, to be used within “Dataclasses with Declarative Table” style mappings.

### Mapping pre-existing dataclasses using Imperative Mapping

As described previously, a class which is set up as a dataclass using the `@dataclass` decorator can then be further decorated using the `registry.mapped()` decorator in order to apply declarative-style mapping to the class. As an alternative to using the `registry.mapped()` decorator, we may also pass the class through the `registry.map_imperatively()` method instead, so that we may pass all `Table` and `Mapper` configuration imperatively to the function rather than having them defined on the class itself as class variables:

    from __future__ import annotations

    from dataclasses import dataclass
    from dataclasses import field
    from typing import List

    from sqlalchemy import Column
    from sqlalchemy import ForeignKey
    from sqlalchemy import Integer
    from sqlalchemy import MetaData
    from sqlalchemy import String
    from sqlalchemy import Table
    from sqlalchemy.orm import registry
    from sqlalchemy.orm import relationship

    mapper_registry = registry()

    @dataclass
    class User:
        id: int = field(init=False)
        name: str = None
        fullname: str = None
        nickname: str = None
        addresses: List[Address] = field(default_factory=list)

    @dataclass
    class Address:
        id: int = field(init=False)
        user_id: int = field(init=False)
        email_address: str = None

    metadata_obj = MetaData()

    user = Table(
        "user",
        metadata_obj,
        Column("id", Integer, primary_key=True),
        Column("name", String(50)),
        Column("fullname", String(50)),
        Column("nickname", String(12)),
    )

    address = Table(
        "address",
        metadata_obj,
        Column("id", Integer, primary_key=True),
        Column("user_id", Integer, ForeignKey("user.id")),
        Column("email_address", String(50)),
    )

    mapper_registry.map_imperatively(
        User,
        user,
        properties={
            "addresses": relationship(Address, backref="user", order_by=address.c.id),
        },
    )

    mapper_registry.map_imperatively(Address, address)

The same warning mentioned in Mapping pre-existing dataclasses using Declarative With Imperative Table applies when using this mapping style.

## Applying ORM mappings to an existing attrs class

Warning

The `attrs` library is not part of SQLAlchemy’s continuous integration testing, and compatibility with this library may change without notice due to incompatibilities introduced by either side.

The attrs library is a popular third party library that provides similar features as dataclasses, with many additional features provided not found in ordinary dataclasses.

A class augmented with attrs uses the `@define` decorator. This decorator initiates a process to scan the class for attributes that define the class’ behavior, which are then used to generate methods, documentation, and annotations.

The SQLAlchemy ORM supports mapping an attrs class using Imperative mapping. The general form of this style is equivalent to the Mapping pre-existing dataclasses using Imperative Mapping mapping form used with dataclasses, where the class construction uses `attrs` alone, with ORM mappings applied after the fact without any class attribute scanning.

The `@define` decorator of attrs by default replaces the annotated class with a new __slots__ based class, which is not supported. When using the old style annotation `@attr.s` or using `define(slots=False)`, the class does not get replaced. Furthermore `attrs` removes its own class-bound attributes after the decorator runs, so that SQLAlchemy’s mapping process takes over these attributes without any issue. Both decorators, `@attr.s` and `@define(slots=False)` work with SQLAlchemy.

Changed in version 2.0: SQLAlchemy integration with `attrs` works only with imperative mapping style, that is, not using Declarative. The introduction of ORM Annotated Declarative style is not cross-compatible with `attrs`.

The `attrs` class is built first. The SQLAlchemy ORM mapping can be applied after the fact using `registry.map_imperatively()`:

    from __future__ import annotations

    from typing import List

    from attrs import define
    from sqlalchemy import Column
    from sqlalchemy import ForeignKey
    from sqlalchemy import Integer
    from sqlalchemy import MetaData
    from sqlalchemy import String
    from sqlalchemy import Table
    from sqlalchemy.orm import registry
    from sqlalchemy.orm import relationship

    mapper_registry = registry()

    @define(slots=False)
    class User:
        id: int
        name: str
        fullname: str
        nickname: str
        addresses: List[Address]

    @define(slots=False)
    class Address:
        id: int
        user_id: int
        email_address: Optional[str]

    metadata_obj = MetaData()

    user = Table(
        "user",
        metadata_obj,
        Column("id", Integer, primary_key=True),
        Column("name", String(50)),
        Column("fullname", String(50)),
        Column("nickname", String(12)),
    )

    address = Table(
        "address",
        metadata_obj,
        Column("id", Integer, primary_key=True),
        Column("user_id", Integer, ForeignKey("user.id")),
        Column("email_address", String(50)),
    )

    mapper_registry.map_imperatively(
        User,
        user,
        properties={
            "addresses": relationship(Address, backref="user", order_by=address.c.id),
        },
    )

    mapper_registry.map_imperatively(Address, address)