Composite Column Types — SQLAlchemy 2.0 Documentation

Release: 2.0.42 | Release Date: July 29, 2025

# SQLAlchemy 2.0 Documentation

# Composite Column Types

Sets of columns can be associated with a single user-defined datatype, which in modern use is normally a Python dataclass. The ORM provides a single attribute which represents the group of columns using the class you provide.

A simple example represents pairs of `Integer` columns as a `Point` object, with attributes `.x` and `.y`. Using a dataclass, these attributes are defined with the corresponding `int` Python type:

    import dataclasses

    @dataclasses.dataclass
    class Point:
        x: int
        y: int

Non-dataclass forms are also accepted, but require additional methods to be implemented. For an example using a non-dataclass class, see the section Using Legacy Non-Dataclasses.

Added in version 2.0: The `composite()` construct fully supports Python dataclasses including the ability to derive mapped column datatypes from the composite class.

We will create a mapping to a table `vertices`, which represents two points as `x1/y1` and `x2/y2`. The `Point` class is associated with the mapped columns using the `composite()` construct.

The example below illustrates the most modern form of `composite()` as used with a fully Annotated Declarative Table configuration. `mapped_column()` constructs representing each column are passed directly to `composite()`, indicating zero or more aspects of the columns to be generated, in this case the names; the `composite()` construct derives the column types (in this case `int`, corresponding to `Integer`) from the dataclass directly:

    from sqlalchemy.orm import DeclarativeBase, Mapped
    from sqlalchemy.orm import composite, mapped_column

    class Base(DeclarativeBase):
        pass

    class Vertex(Base):
        __tablename__ = "vertices"

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

        start: Mapped[Point] = composite(mapped_column("x1"), mapped_column("y1"))
        end: Mapped[Point] = composite(mapped_column("x2"), mapped_column("y2"))

        def __repr__(self):
            return f"Vertex(start={self.start}, end={self.end})"

Tip

In the example above the columns that represent the composites (`x1`, `y1`, etc.) are also accessible on the class but are not correctly understood by type checkers. If accessing the single columns is important they can be explicitly declared, as shown in Map columns directly, pass attribute names to composite.

The above mapping would correspond to a CREATE TABLE statement as:

    >>> from sqlalchemy.schema import CreateTable
    >>> print(CreateTable(Vertex.__table__))
    CREATE TABLE vertices (
      id INTEGER NOT NULL,
      x1 INTEGER NOT NULL,
      y1 INTEGER NOT NULL,
      x2 INTEGER NOT NULL,
      y2 INTEGER NOT NULL,
      PRIMARY KEY (id)
    )

## Working with Mapped Composite Column Types

With a mapping as illustrated in the top section, we can work with the `Vertex` class, where the `.start` and `.end` attributes will transparently refer to the columns referenced by the `Point` class, as well as with instances of the `Vertex` class, where the `.start` and `.end` attributes will refer to instances of the `Point` class. The `x1`, `y1`, `x2`, and `y2` columns are handled transparently:

- Persisting Point objects

  We can create a `Vertex` object, assign `Point` objects as members, and they will be persisted as expected:

      >>> v = Vertex(start=Point(3, 4), end=Point(5, 6))
      >>> session.add(v)
      >>> session.commit()
      BEGIN (implicit)
      INSERT INTO vertices (x1, y1, x2, y2) VALUES (?, ?, ?, ?)
      [generated in ...] (3, 4, 5, 6)
      COMMIT

- Selecting Point objects as columns

  `composite()` will allow the `Vertex.start` and `Vertex.end` attributes to behave like a single SQL expression to as much an extent as possible when using the ORM `Session` (including the legacy `Query` object) to select `Point` objects:

      >>> stmt = select(Vertex.start, Vertex.end)
      >>> session.execute(stmt).all()
      SELECT vertices.x1, vertices.y1, vertices.x2, vertices.y2
      FROM vertices
      [...] ()
      [(Point(x=3, y=4), Point(x=5, y=6))]

- Comparing Point objects in SQL expressions

  The `Vertex.start` and `Vertex.end` attributes may be used in WHERE criteria and similar, using ad-hoc `Point` objects for comparisons:

      >>> stmt = select(Vertex).where(Vertex.start == Point(3, 4)).where(Vertex.end < Point(7, 8))
      >>> session.scalars(stmt).all()
      SELECT vertices.id, vertices.x1, vertices.y1, vertices.x2, vertices.y2
      FROM vertices
      WHERE vertices.x1 = ? AND vertices.y1 = ? AND vertices.x2 < ? AND vertices.y2 < ?
      [...] (3, 4, 7, 8)
      [Vertex(Point(x=3, y=4), Point(x=5, y=6))]

  Added in version 2.0: `composite()` constructs now support “ordering” comparisons such as `<`, `>=`, and similar, in addition to the already-present support for `==`, `!=`.

  Tip

  The “ordering” comparison above using the “less than” operator (`<`) as well as the “equality” comparison using `==`, when used to generate SQL expressions, are implemented by the `Comparator` class, and don’t make use of the comparison methods on the composite class itself, e.g. the `__lt__()` or `__eq__()` methods. From this it follows that the `Point` dataclass above also need not implement the dataclasses `order=True` parameter for the above SQL operations to work. The section Redefining Comparison Operations for Composites contains background on how to customize the comparison operations.

- Updating Point objects on Vertex Instances

  By default, the `Point` object must be replaced by a new object for changes to be detected:

      >>> v1 = session.scalars(select(Vertex)).one()
      SELECT vertices.id, vertices.x1, vertices.y1, vertices.x2, vertices.y2
      FROM vertices
      [...] ()
      >>> v1.end = Point(x=10, y=14)
      >>> session.commit()
      UPDATE vertices SET x2=?, y2=? WHERE vertices.id = ?
      [...] (10, 14, 1)
      COMMIT

  In order to allow in place changes on the composite object, the Mutation Tracking extension must be used. See the section Establishing Mutability on Composites for examples.

## Other mapping forms for composites

The `composite()` construct may be passed the relevant columns using a `mapped_column()` construct, a `Column`, or the string name of an existing mapped column. The following examples illustrate an equivalent mapping as that of the main section above.

### Map columns directly, then pass to composite

Here we pass the existing `mapped_column()` instances to the `composite()` construct, as in the non-annotated example below where we also pass the `Point` class as the first argument to `composite()`:

    from sqlalchemy import Integer
    from sqlalchemy.orm import mapped_column, composite

    class Vertex(Base):
        __tablename__ = "vertices"

        id = mapped_column(Integer, primary_key=True)
        x1 = mapped_column(Integer)
        y1 = mapped_column(Integer)
        x2 = mapped_column(Integer)
        y2 = mapped_column(Integer)

        start = composite(Point, x1, y1)
        end = composite(Point, x2, y2)

### Map columns directly, pass attribute names to composite

We can write the same example above using more annotated forms where we have the option to pass attribute names to `composite()` instead of full column constructs:

    from sqlalchemy.orm import mapped_column, composite, Mapped

    class Vertex(Base):
        __tablename__ = "vertices"

        id: Mapped[int] = mapped_column(primary_key=True)
        x1: Mapped[int]
        y1: Mapped[int]
        x2: Mapped[int]
        y2: Mapped[int]

        start: Mapped[Point] = composite("x1", "y1")
        end: Mapped[Point] = composite("x2", "y2")

### Imperative mapping and imperative table

When using imperative table or fully imperative mappings, we have access to `Column` objects directly. These may be passed to `composite()` as well, as in the imperative example below:

    mapper_registry.map_imperatively(
        Vertex,
        vertices_table,
        properties={
            "start": composite(Point, vertices_table.c.x1, vertices_table.c.y1),
            "end": composite(Point, vertices_table.c.x2, vertices_table.c.y2),
        },
    )

## Using Legacy Non-Dataclasses

If not using a dataclass, the requirements for the custom datatype class are that it have a constructor which accepts positional arguments corresponding to its column format, and also provides a method `__composite_values__()` which returns the state of the object as a list or tuple, in order of its column-based attributes. It also should supply adequate `__eq__()` and `__ne__()` methods which test the equality of two instances.

To illustrate the equivalent `Point` class from the main section not using a dataclass:

    class Point:
        def __init__(self, x, y):
            self.x = x
            self.y = y

        def __composite_values__(self):
            return self.x, self.y

        def __repr__(self):
            return f"Point(x={self.x!r}, y={self.y!r})"

        def __eq__(self, other):
            return isinstance(other, Point) and other.x == self.x and other.y == self.y

        def __ne__(self, other):
            return not self.__eq__(other)

Usage with `composite()` then proceeds where the columns to be associated with the `Point` class must also be declared with explicit types, using one of the forms at Other mapping forms for composites.

## Tracking In-Place Mutations on Composites

In-place changes to an existing composite value are not tracked automatically. Instead, the composite class needs to provide events to its parent object explicitly. This task is largely automated via the usage of the `MutableComposite` mixin, which uses events to associate each user-defined composite object with all parent associations. Please see the example in Establishing Mutability on Composites.

## Redefining Comparison Operations for Composites

The “equals” comparison operation by default produces an AND of all corresponding columns equated to one another. This can be changed using the `comparator_factory` argument to `composite()`, where we specify a custom `Comparator` class to define existing or new operations. Below we illustrate the “greater than” operator, implementing the same expression that the base “greater than” does:

    import dataclasses

    from sqlalchemy.orm import composite
    from sqlalchemy.orm import CompositeProperty
    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.sql import and_

    @dataclasses.dataclass
    class Point:
        x: int
        y: int

    class PointComparator(CompositeProperty.Comparator):
        def __gt__(self, other):
            """redefine the 'greater than' operation"""

            return and_(
                *[
                    a > b
                    for a, b in zip(
                        self.__clause_element__().clauses,
                        dataclasses.astuple(other),
                    )
                ]
            )

    class Base(DeclarativeBase):
        pass

    class Vertex(Base):
        __tablename__ = "vertices"

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

        start: Mapped[Point] = composite(
            mapped_column("x1"), mapped_column("y1"), comparator_factory=PointComparator
        )
        end: Mapped[Point] = composite(
            mapped_column("x2"), mapped_column("y2"), comparator_factory=PointComparator
        )

Since `Point` is a dataclass, we may make use of `dataclasses.astuple()` to get a tuple form of `Point` instances.

The custom comparator then returns the appropriate SQL expression:

    >>> print(Vertex.start > Point(5, 6))
    vertices.x1 > :x1_1 AND vertices.y1 > :y1_1

## Nesting Composites

Composite objects can be defined to work in simple nested schemes, by redefining behaviors within the composite class to work as desired, then mapping the composite class to the full length of individual columns normally. This requires that additional methods to move between the “nested” and “flat” forms are defined.

Below we reorganize the `Vertex` class to itself be a composite object which refers to `Point` objects. `Vertex` and `Point` can be dataclasses, however we will add a custom construction method to `Vertex` that can be used to create new `Vertex` objects given four column values, which will will arbitrarily name `_generate()` and define as a classmethod so that we can make new `Vertex` objects by passing values to the `Vertex._generate()` method.

We will also implement the `__composite_values__()` method, which is a fixed name recognized by the `composite()` construct (introduced previously at Using Legacy Non-Dataclasses) that indicates a standard way of receiving the object as a flat tuple of column values, which in this case will supersede the usual dataclass-oriented methodology.

With our custom `_generate()` constructor and `__composite_values__()` serializer method, we can now move between a flat tuple of columns and `Vertex` objects that contain `Point` instances. The `Vertex._generate` method is passed as the first argument to the `composite()` construct as the source of new `Vertex` instances, and the `__composite_values__()` method will be used implicitly by `composite()`.

For the purposes of the example, the `Vertex` composite is then mapped to a class called `HasVertex`, which is where the `Table` containing the four source columns ultimately resides:

    from __future__ import annotations

    import dataclasses
    from typing import Any
    from typing import Tuple

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

    @dataclasses.dataclass
    class Point:
        x: int
        y: int

    @dataclasses.dataclass
    class Vertex:
        start: Point
        end: Point

        @classmethod
        def _generate(cls, x1: int, y1: int, x2: int, y2: int) -> Vertex:
            """generate a Vertex from a row"""
            return Vertex(Point(x1, y1), Point(x2, y2))

        def __composite_values__(self) -> Tuple[Any, ...]:
            """generate a row from a Vertex"""
            return dataclasses.astuple(self.start) + dataclasses.astuple(self.end)

    class Base(DeclarativeBase):
        pass

    class HasVertex(Base):
        __tablename__ = "has_vertex"
        id: Mapped[int] = mapped_column(primary_key=True)
        x1: Mapped[int]
        y1: Mapped[int]
        x2: Mapped[int]
        y2: Mapped[int]

        vertex: Mapped[Vertex] = composite(Vertex._generate, "x1", "y1", "x2", "y2")

The above mapping can then be used in terms of `HasVertex`, `Vertex`, and `Point`:

    hv = HasVertex(vertex=Vertex(Point(1, 2), Point(3, 4)))

    session.add(hv)
    session.commit()

    stmt = select(HasVertex).where(HasVertex.vertex == Vertex(Point(1, 2), Point(3, 4)))

    hv = session.scalars(stmt).first()
    print(hv.vertex.start)
    print(hv.vertex.end)

## Composite API

Object Name | Description
--- | ---
composite([_class_or_attr], *attrs, [group, deferred, raiseload, comparator_factory, active_history, init, repr, default, default_factory, compare, kw_only, hash, info, doc, dataclass_metadata], **__kw) | Return a composite column-based property for use with a Mapper.

function sqlalchemy.orm.composite(_class_or_attr: None | Type[_CC] | Callable[..., _CC] | _CompositeAttrType[Any] = None, *attrs: _CompositeAttrType[Any], group: str | None = None, deferred: bool = False, raiseload: bool = False, comparator_factory: Type[Composite.Comparator[_T]] | None = None, active_history: bool = False, init: _NoArg | bool = _NoArg.NO_ARG, repr: _NoArg | bool = _NoArg.NO_ARG, default: Any | None = _NoArg.NO_ARG, default_factory: _NoArg | Callable[[], _T] = _NoArg.NO_ARG, compare: _NoArg | bool = _NoArg.NO_ARG, kw_only: _NoArg | bool = _NoArg.NO_ARG, hash: _NoArg | bool | None = _NoArg.NO_ARG, info: _InfoType | None = None, doc: str | None = None, dataclass_metadata: _NoArg | Mapping[Any, Any] | None = _NoArg.NO_ARG, **__kw: Any) → Composite[Any]

  Return a composite column-based property for use with a Mapper.

  See the mapping documentation section Composite Column Types for a full usage example.

  The `MapperProperty` returned by `composite()` is the `Composite`.

  Parameters:

    - class_ – The “composite type” class, or any classmethod or callable which will produce a new instance of the composite object given the column values in order.

    - *attrs –

      List of elements to be mapped, which may include:

      - `Column` objects

      - `mapped_column()` constructs

      - string names of other attributes on the mapped class, which may be any other SQL or object-mapped attribute. This can for example allow a composite that refers to a many-to-one relationship

    - active_history=False – When `True`, indicates that the “previous” value for a scalar attribute should be loaded when replaced, if not already loaded. See the same flag on `column_property()`.

    - group – A group name for this property when marked as deferred.

    - deferred – When True, the column property is “deferred”, meaning that it does not load immediately, and is instead loaded when the attribute is first accessed on an instance. See also `deferred()`.

    - comparator_factory – a class which extends `Comparator` which provides custom SQL clause generation for comparison operations.

    - doc – optional string that will be applied as the doc on the class-bound descriptor.

    - info – Optional data dictionary which will be populated into the `MapperProperty.info` attribute of this object.

    - init – Specific to Declarative Dataclass Mapping, specifies if the mapped attribute should be part of the `__init__()` method as generated by the dataclass process.

    - repr – Specific to Declarative Dataclass Mapping, specifies if the mapped attribute should be part of the `__repr__()` method as generated by the dataclass process.

    - default_factory – Specific to Declarative Dataclass Mapping, specifies a default-value generation function that will take place as part of the `__init__()` method as generated by the dataclass process.

    - compare –

      Specific to Declarative Dataclass Mapping, indicates if this field should be included in comparison operations when generating the `__eq__()` and `__ne__()` methods for the mapped class.

      Added in version 2.0.0b4.

    - kw_only – Specific to Declarative Dataclass Mapping, indicates if this field should be marked as keyword-only when generating the `__init__()`.

    - hash –

      Specific to Declarative Dataclass Mapping, controls if this field is included when generating the `__hash__()` method for the mapped class.

      Added in version 2.0.36.

    - dataclass_metadata –

      Specific to Declarative Dataclass Mapping, supplies metadata to be attached to the generated dataclass field.

      Added in version 2.0.42.