Release: 2.0.45 | Release Date: December 9, 2025

# SQLAlchemy 2.0 Documentation

# Mapper Configuration with Declarative

The section Mapped Class Essential Components discusses the general configurational elements of a `Mapper` construct, which is the structure that defines how a particular user defined class is mapped to a database table or other SQL construct. The following sections describe specific details about how the declarative system goes about constructing the `Mapper`.

## Defining Mapped Properties with Declarative

The examples given at Table Configuration with Declarative illustrate mappings against table-bound columns, using the `mapped_column()` construct. There are several other varieties of ORM mapped constructs that may be configured besides table-bound columns, the most common being the `relationship()` construct. Other kinds of properties include SQL expressions that are defined using the `column_property()` construct and multiple-column mappings using the `composite()` construct.

While an imperative mapping makes use of the properties dictionary to establish all the mapped class attributes, in the declarative mapping, these properties are all specified inline with the class definition, which in the case of a declarative table mapping are inline with the `Column` objects that will be used to generate a `Table` object.

Working with the example mapping of `User` and `Address`, we may illustrate a declarative table mapping that includes not just `mapped_column()` objects but also relationships and SQL expressions:

    from typing import List
    from typing import Optional

    from sqlalchemy import Column
    from sqlalchemy import ForeignKey
    from sqlalchemy import String
    from sqlalchemy import Text
    from sqlalchemy.orm import column_property
    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[int] = mapped_column(primary_key=True)
        name: Mapped[str]
        firstname: Mapped[str] = mapped_column(String(50))
        lastname: Mapped[str] = mapped_column(String(50))
        fullname: Mapped[str] = column_property(firstname + " " + lastname)

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

    class Address(Base):
        __tablename__ = "address"

        id: Mapped[int] = mapped_column(primary_key=True)
        user_id: Mapped[int] = mapped_column(ForeignKey("user.id"))
        email_address: Mapped[str]
        address_statistics: Mapped[Optional[str]] = mapped_column(Text, deferred=True)

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

The above declarative table mapping features two tables, each with a `relationship()` referring to the other, as well as a simple SQL expression mapped by `column_property()`, and an additional `mapped_column()` that indicates loading should be on a “deferred” basis as defined by the `mapped_column.deferred` keyword. More documentation on these particular concepts may be found at Basic Relationship Patterns, Using column_property, and Limiting which Columns Load with Column Deferral.

Properties may be specified with a declarative mapping as above using “hybrid table” style as well; the `Column` objects that are directly part of a table move into the `Table` definition but everything else, including composed SQL expressions, would still be inline with the class definition. Constructs that need to refer to a `Column` directly would reference it in terms of the `Table` object. To illustrate the above mapping using hybrid table style:

    # mapping attributes using declarative with imperative table
    # i.e. __table__

    from sqlalchemy import Column, ForeignKey, Integer, String, Table, Text
    from sqlalchemy.orm import column_property
    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import deferred
    from sqlalchemy.orm import relationship

    class Base(DeclarativeBase):
        pass

    class User(Base):
        __table__ = Table(
            "user",
            Base.metadata,
            Column("id", Integer, primary_key=True),
            Column("name", String),
            Column("firstname", String(50)),
            Column("lastname", String(50)),
        )

        fullname = column_property(__table__.c.firstname + " " + __table__.c.lastname)

        addresses = relationship("Address", back_populates="user")

    class Address(Base):
        __table__ = Table(
            "address",
            Base.metadata,
            Column("id", Integer, primary_key=True),
            Column("user_id", ForeignKey("user.id")),
            Column("email_address", String),
            Column("address_statistics", Text),
        )

        address_statistics = deferred(__table__.c.address_statistics)

        user = relationship("User", back_populates="addresses")

Things to note above:

- The address `Table` contains a column called `address_statistics`, however we re-map this column under the same attribute name to be under the control of a `deferred()` construct.

- With both declararative table and hybrid table mappings, when we define a `ForeignKey` construct, we always name the target table using the table name, and not the mapped class name.

- When we define `relationship()` constructs, as these constructs create a linkage between two mapped classes where one necessarily is defined before the other, we can refer to the remote class using its string name. This functionality also extends into the area of other arguments specified on the `relationship()` such as the “primary join” and “order by” arguments. See the section Late-Evaluation of Relationship Arguments for details on this.

## Mapper Configuration Options with Declarative

With all mapping forms, the mapping of the class is configured through parameters that become part of the `Mapper` object. The function which ultimately receives these arguments is the `Mapper` function, and are delivered to it from one of the front-facing mapping functions defined on the `registry` object.

For the declarative form of mapping, mapper arguments are specified using the `__mapper_args__` declarative class variable, which is a dictionary that is passed as keyword arguments to the `Mapper` function. Some examples:

Map Specific Primary Key Columns

The example below illustrates Declarative-level settings for the `Mapper.primary_key` parameter, which establishes particular columns as part of what the ORM should consider to be a primary key for the class, independently of schema-level primary key constraints:

    class GroupUsers(Base):
        __tablename__ = "group_users"

        user_id = mapped_column(String(40))
        group_id = mapped_column(String(40))

        __mapper_args__ = {"primary_key": [user_id, group_id]}

See also

Mapping to an Explicit Set of Primary Key Columns - further background on ORM mapping of explicit columns as primary key columns

Version ID Column

The example below illustrates Declarative-level settings for the `Mapper.version_id_col` and `Mapper.version_id_generator` parameters, which configure an ORM-maintained version counter that is updated and checked within the unit of work flush process:

    from datetime import datetime

    class Widget(Base):
        __tablename__ = "widgets"

        id = mapped_column(Integer, primary_key=True)
        timestamp = mapped_column(DateTime, nullable=False)

        __mapper_args__ = {
            "version_id_col": timestamp,
            "version_id_generator": lambda v: datetime.now(),
        }

See also

Configuring a Version Counter - background on the ORM version counter feature

Single Table Inheritance

The example below illustrates Declarative-level settings for the `Mapper.polymorphic_on` and `Mapper.polymorphic_identity` parameters, which are used when configuring a single-table inheritance mapping:

    class Person(Base):
        __tablename__ = "person"

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

        __mapper_args__ = dict(
            polymorphic_on=type,
            polymorphic_identity="person",
        )

    class Employee(Person):
        __mapper_args__ = dict(
            polymorphic_identity="employee",
        )

See also

Single Table Inheritance - background on the ORM single table inheritance mapping feature.

### Constructing mapper arguments dynamically

The `__mapper_args__` dictionary may be generated from a class-bound descriptor method rather than from a fixed dictionary by making use of the `declared_attr()` construct. This is useful to create arguments for mappers that are programmatically derived from the table configuration or other aspects of the mapped class. A dynamic `__mapper_args__` attribute will typically be useful when using a Declarative Mixin or abstract base class.

For example, to omit from the mapping any columns that have a special `Column.info` value, a mixin can use a `__mapper_args__` method that scans for these columns from the `cls.__table__` attribute and passes them to the `Mapper.exclude_properties` collection:

    from sqlalchemy import Column
    from sqlalchemy import Integer
    from sqlalchemy import select
    from sqlalchemy import String
    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import declared_attr

    class ExcludeColsWFlag:
        @declared_attr
        def __mapper_args__(cls):
            return {
                "exclude_properties": [
                    column.key
                    for column in cls.__table__.c
                    if column.info.get("exclude", False)
                ]
            }

    class Base(DeclarativeBase):
        pass

    class SomeClass(ExcludeColsWFlag, Base):
        __tablename__ = "some_table"

        id = mapped_column(Integer, primary_key=True)
        data = mapped_column(String)
        not_needed = mapped_column(String, info={"exclude": True})

Above, the `ExcludeColsWFlag` mixin provides a per-class `__mapper_args__` hook that will scan for `Column` objects that include the key/value `'exclude': True` passed to the `Column.info` parameter, and then add their string “key” name to the `Mapper.exclude_properties` collection which will prevent the resulting `Mapper` from considering these columns for any SQL operations.

See also

Composing Mapped Hierarchies with Mixins

## Other Declarative Mapping Directives

### `__declare_last__()`

The `__declare_last__()` hook allows definition of a class level function that is automatically called by the `MapperEvents.after_configured()` event, which occurs after mappings are assumed to be completed and the ‘configure’ step has finished:

    class MyClass(Base):
        @classmethod
        def __declare_last__(cls):
            """ """
            # do something with mappings

### `__declare_first__()`

Like `__declare_last__()`, but is called at the beginning of mapper configuration via the `MapperEvents.before_configured()` event:

    class MyClass(Base):
        @classmethod
        def __declare_first__(cls):
            """ """
            # do something before mappings are configured

### `metadata`

The `MetaData` collection normally used to assign a new `Table` is the `registry.metadata` attribute associated with the `registry` object in use. When using a declarative base class such as that produced by the `DeclarativeBase` superclass, as well as legacy functions such as `declarative_base()` and `registry.generate_base()`, this `MetaData` is also normally present as an attribute named `.metadata` that’s directly on the base class, and thus also on the mapped class via inheritance. Declarative uses this attribute, when present, in order to determine the target `MetaData` collection, or if not present, uses the `MetaData` associated directly with the `registry`.

This attribute may also be assigned towards in order to affect the `MetaData` collection to be used on a per-mapped-hierarchy basis for a single base and/or `registry`. This takes effect whether a declarative base class is used or if the `registry.mapped()` decorator is used directly, thus allowing patterns such as the metadata-per-abstract base example in the next section, __abstract__. A similar pattern can be illustrated using `registry.mapped()` as follows:

    reg = registry()

    class BaseOne:
        metadata = MetaData()

    class BaseTwo:
        metadata = MetaData()

    @reg.mapped
    class ClassOne:
        __tablename__ = "t1"  # will use reg.metadata

        id = mapped_column(Integer, primary_key=True)

    @reg.mapped
    class ClassTwo(BaseOne):
        __tablename__ = "t1"  # will use BaseOne.metadata

        id = mapped_column(Integer, primary_key=True)

    @reg.mapped
    class ClassThree(BaseTwo):
        __tablename__ = "t1"  # will use BaseTwo.metadata

        id = mapped_column(Integer, primary_key=True)

See also

__abstract__

### `__abstract__`

`__abstract__` causes declarative to skip the production of a table or mapper for the class entirely. A class can be added within a hierarchy in the same way as mixin (see Mixin and Custom Base Classes), allowing subclasses to extend just from the special class:

    class SomeAbstractBase(Base):
        __abstract__ = True

        def some_helpful_method(self):
            """ """

        @declared_attr
        def __mapper_args__(cls):
            return {"helpful mapper arguments": True}

    class MyMappedClass(SomeAbstractBase):
        pass

One possible use of `__abstract__` is to use a distinct `MetaData` for different bases:

    class Base(DeclarativeBase):
        pass

    class DefaultBase(Base):
        __abstract__ = True
        metadata = MetaData()

    class OtherBase(Base):
        __abstract__ = True
        metadata = MetaData()

Above, classes which inherit from `DefaultBase` will use one `MetaData` as the registry of tables, and those which inherit from `OtherBase` will use a different one. The tables themselves can then be created perhaps within distinct databases:

    DefaultBase.metadata.create_all(some_engine)
    OtherBase.metadata.create_all(some_other_engine)

See also

Building Deeper Hierarchies with polymorphic_abstract - an alternative form of “abstract” mapped class that is appropriate for inheritance hierarchies.

### `__table_cls__`

Allows the callable / class used to generate a `Table` to be customized. This is a very open-ended hook that can allow special customizations to a `Table` that one generates here:

    class MyMixin:
        @classmethod
        def __table_cls__(cls, name, metadata_obj, *arg, **kw):
            return Table(f"my_{name}", metadata_obj, *arg, **kw)

The above mixin would cause all `Table` objects generated to include the prefix `"my_"`, followed by the name normally specified using the `__tablename__` attribute.

`__table_cls__` also supports the case of returning `None`, which causes the class to be considered as single-table inheritance vs. its subclass. This may be useful in some customization schemes to determine that single-table inheritance should take place based on the arguments for the table itself, such as, define as single-inheritance if there is no primary key present:

    class AutoTable:
        @declared_attr
        def __tablename__(cls):
            return cls.__name__

        @classmethod
        def __table_cls__(cls, *arg, **kw):
            for obj in arg[1:]:
                if (isinstance(obj, Column) and obj.primary_key) or isinstance(
                    obj, PrimaryKeyConstraint
                ):
                    return Table(*arg, **kw)

            return None

    class Person(AutoTable, Base):
        id = mapped_column(Integer, primary_key=True)

    class Employee(Person):
        employee_name = mapped_column(String)

The above `Employee` class would be mapped as single-table inheritance against `Person`; the `employee_name` column would be added as a member of the `Person` table.