Declarative Mapping Styles — SQLAlchemy 2.0 Documentation

Release: 2.0.51 | Release Date: June 15, 2026

# SQLAlchemy 2.0 Documentation

# Declarative Mapping Styles

As introduced at Declarative Mapping, the Declarative Mapping is the typical way that mappings are constructed in modern SQLAlchemy. This section will provide an overview of forms that may be used for Declarative mapper configuration.

## Using a Declarative Base Class

The most common approach is to generate a “Declarative Base” class by subclassing the `DeclarativeBase` superclass:

    from sqlalchemy.orm import DeclarativeBase

    # declarative base class
    class Base(DeclarativeBase):
        pass

The Declarative Base class may also be created given an existing `registry` by assigning it as a class variable named `registry`:

    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import registry

    reg = registry()

    # declarative base class
    class Base(DeclarativeBase):
        registry = reg

Changed in version 2.0: The `DeclarativeBase` superclass supersedes the use of the `declarative_base()` function and `registry.generate_base()` methods; the superclass approach integrates with PEP 484 tools without the use of plugins. See ORM Declarative Models for migration notes.

With the declarative base class, new mapped classes are declared as subclasses of the base:

    from datetime import datetime
    from typing import List
    from typing import Optional

    from sqlalchemy import ForeignKey
    from sqlalchemy import func
    from sqlalchemy import Integer
    from sqlalchemy import String
    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import relationship

    class Base(DeclarativeBase):
        pass

    class User(Base):
        __tablename__ = "user"

        id = mapped_column(Integer, primary_key=True)
        name: Mapped[str]
        fullname: Mapped[Optional[str]]
        nickname: Mapped[Optional[str]] = mapped_column(String(64))
        create_date: Mapped[datetime] = mapped_column(insert_default=func.now())

        addresses: Mapped[List["Address"]] = relationship(back_populates="user")

    class Address(Base):
        __tablename__ = "address"

        id = mapped_column(Integer, primary_key=True)
        user_id = mapped_column(ForeignKey("user.id"))
        email_address: Mapped[str]

        user: Mapped["User"] = relationship(back_populates="addresses")

Above, the `Base` class serves as a base for new classes that are to be mapped, as above new mapped classes `User` and `Address` are constructed.

For each subclass constructed, the body of the class then follows the declarative mapping approach which defines both a `Table` as well as a `Mapper` object behind the scenes which comprise a full mapping.

See also

Table Configuration with Declarative - describes how to specify the components of the mapped `Table` to be generated, including notes and options on the use of the `mapped_column()` construct and how it interacts with the `Mapped` annotation type

Mapper Configuration with Declarative - describes all other aspects of ORM mapper configuration within Declarative including `relationship()` configuration, SQL expressions and `Mapper` parameters

## Declarative Mapping using a Decorator (no declarative base)

As an alternative to using the “declarative base” class is to apply declarative mapping to a class explicitly, using either an imperative technique similar to that of a “classical” mapping, or more succinctly by using a decorator. The `registry.mapped()` function is a class decorator that can be applied to any Python class with no hierarchy in place. The Python class otherwise is configured in declarative style normally.

The example below sets up the identical mapping as seen in the previous section, using the `registry.mapped()` decorator rather than using the `DeclarativeBase` superclass:

    from datetime import datetime
    from typing import List
    from typing import Optional

    from sqlalchemy import ForeignKey
    from sqlalchemy import func
    from sqlalchemy import Integer
    from sqlalchemy import String
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import registry
    from sqlalchemy.orm import relationship

    mapper_registry = registry()

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

        id = mapped_column(Integer, primary_key=True)
        name: Mapped[str]
        fullname: Mapped[Optional[str]]
        nickname: Mapped[Optional[str]] = mapped_column(String(64))
        create_date: Mapped[datetime] = mapped_column(insert_default=func.now())

        addresses: Mapped[List["Address"]] = relationship(back_populates="user")

    @mapper_registry.mapped
    class Address:
        __tablename__ = "address"

        id = mapped_column(Integer, primary_key=True)
        user_id = mapped_column(ForeignKey("user.id"))
        email_address: Mapped[str]

        user: Mapped["User"] = relationship(back_populates="addresses")

When using the above style, the mapping of a particular class will only proceed if the decorator is applied to that class directly. For inheritance mappings (described in detail at Mapping Class Inheritance Hierarchies), the decorator should be applied to each subclass that is to be mapped:

    from sqlalchemy.orm import registry

    mapper_registry = registry()

    @mapper_registry.mapped
    class Person:
        __tablename__ = "person"

        person_id = mapped_column(Integer, primary_key=True)
        type = mapped_column(String, nullable=False)

        __mapper_args__ = {
            "polymorphic_on": type,
            "polymorphic_identity": "person",
        }

    @mapper_registry.mapped
    class Employee(Person):
        __tablename__ = "employee"

        person_id = mapped_column(ForeignKey("person.person_id"), primary_key=True)

        __mapper_args__ = {
            "polymorphic_identity": "employee",
        }

Both the declarative table and imperative table table configuration styles may be used with either the Declarative Base or decorator styles of Declarative mapping.

The decorator form of mapping is useful when combining a SQLAlchemy declarative mapping with other class instrumentation systems such as dataclasses and attrs, though note that SQLAlchemy 2.0 now features dataclasses integration with Declarative Base classes as well.