Release: 2.0.37 | Release Date: January 9, 2025

# SQLAlchemy 2.0 Documentation

# Basic Relationship Patterns

A quick walkthrough of the basic relational patterns, which in this section are illustrated using Declarative style mappings based on the use of the `Mapped` annotation type.

The setup for each of the following sections is as follows:

    from __future__ import annotations
    from typing import List

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

    class Base(DeclarativeBase):
        pass

## Declarative vs. Imperative Forms

As SQLAlchemy has evolved, different ORM configurational styles have emerged. For examples in this section and others that use annotated Declarative mappings with `Mapped`, the corresponding non-annotated form should use the desired class, or string class name, as the first argument passed to `relationship()`. The example below illustrates the form used in this document, which is a fully Declarative example using PEP 484 annotations, where the `relationship()` construct is also deriving the target class and collection type from the `Mapped` annotation, which is the most modern form of SQLAlchemy Declarative mapping:

    class Parent(Base):
        __tablename__ = "parent_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        children: Mapped[List["Child"]] = relationship(back_populates="parent")

    class Child(Base):
        __tablename__ = "child_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        parent_id: Mapped[int] = mapped_column(ForeignKey("parent_table.id"))
        parent: Mapped["Parent"] = relationship(back_populates="children")

In contrast, using a Declarative mapping without annotations is the more “classic” form of mapping, where `relationship()` requires all parameters passed to it directly, as in the example below:

    class Parent(Base):
        __tablename__ = "parent_table"

        id = mapped_column(Integer, primary_key=True)
        children = relationship("Child", back_populates="parent")

    class Child(Base):
        __tablename__ = "child_table"

        id = mapped_column(Integer, primary_key=True)
        parent_id = mapped_column(ForeignKey("parent_table.id"))
        parent = relationship("Parent", back_populates="children")

Finally, using Imperative Mapping, which is SQLAlchemy’s original mapping form before Declarative was made (which nonetheless remains preferred by a vocal minority of users), the above configuration looks like:

    registry.map_imperatively(
        Parent,
        parent_table,
        properties={"children": relationship("Child", back_populates="parent")},
    )

    registry.map_imperatively(
        Child,
        child_table,
        properties={"parent": relationship("Parent", back_populates="children")},
    )

Additionally, the default collection style for non-annotated mappings is `list`. To use a `set` or other collection without annotations, indicate it using the `relationship.collection_class` parameter:

    class Parent(Base):
        __tablename__ = "parent_table"

        id = mapped_column(Integer, primary_key=True)
        children = relationship("Child", collection_class=set, ...)

Detail on collection configuration for `relationship()` is at Customizing Collection Access.

Additional differences between annotated and non-annotated / imperative styles will be noted as needed.

## One To Many

A one to many relationship places a foreign key on the child table referencing the parent. `relationship()` is then specified on the parent, as referencing a collection of items represented by the child:

    class Parent(Base):
        __tablename__ = "parent_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        children: Mapped[List["Child"]] = relationship()

    class Child(Base):
        __tablename__ = "child_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        parent_id: Mapped[int] = mapped_column(ForeignKey("parent_table.id"))

To establish a bidirectional relationship in one-to-many, where the “reverse” side is a many to one, specify an additional `relationship()` and connect the two using the `relationship.back_populates` parameter, using the attribute name of each `relationship()` as the value for `relationship.back_populates` on the other:

    class Parent(Base):
        __tablename__ = "parent_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        children: Mapped[List["Child"]] = relationship(back_populates="parent")

    class Child(Base):
        __tablename__ = "child_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        parent_id: Mapped[int] = mapped_column(ForeignKey("parent_table.id"))
        parent: Mapped["Parent"] = relationship(back_populates="children")

`Child` will get a `parent` attribute with many-to-one semantics.

### Using Sets, Lists, or other Collection Types for One To Many

Using annotated Declarative mappings, the type of collection used for the `relationship()` is derived from the collection type passed to the `Mapped` container type. The example from the previous section may be written to use a `set` rather than a `list` for the `Parent.children` collection using `Mapped[Set["Child"]]`:

    class Parent(Base):
        __tablename__ = "parent_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        children: Mapped[Set["Child"]] = relationship(back_populates="parent")

When using non-annotated forms including imperative mappings, the Python class to use as a collection may be passed using the `relationship.collection_class` parameter.

See also

Customizing Collection Access - contains further detail on collection configuration including some techniques to map `relationship()` to dictionaries.

### Configuring Delete Behavior for One to Many

It is often the case that all `Child` objects should be deleted when their owning `Parent` is deleted. To configure this behavior, the `delete` cascade option described at delete is used. An additional option is that a `Child` object can itself be deleted when it is deassociated from its parent. This behavior is described at delete-orphan.

See also

delete

Using foreign key ON DELETE cascade with ORM relationships

delete-orphan

## Many To One

Many to one places a foreign key in the parent table referencing the child. `relationship()` is declared on the parent, where a new scalar-holding attribute will be created:

    class Parent(Base):
        __tablename__ = "parent_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        child_id: Mapped[int] = mapped_column(ForeignKey("child_table.id"))
        child: Mapped["Child"] = relationship()

    class Child(Base):
        __tablename__ = "child_table"

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

The above example shows a many-to-one relationship that assumes non-nullable behavior; the next section, Nullable Many-to-One, illustrates a nullable version.

Bidirectional behavior is achieved by adding a second `relationship()` and applying the `relationship.back_populates` parameter in both directions, using the attribute name of each `relationship()` as the value for `relationship.back_populates` on the other:

    class Parent(Base):
        __tablename__ = "parent_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        child_id: Mapped[int] = mapped_column(ForeignKey("child_table.id"))
        child: Mapped["Child"] = relationship(back_populates="parents")

    class Child(Base):
        __tablename__ = "child_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        parents: Mapped[List["Parent"]] = relationship(back_populates="child")

### Nullable Many-to-One

In the preceding example, the `Parent.child` relationship is not typed as allowing `None`; this follows from the `Parent.child_id` column itself not being nullable, as it is typed with `Mapped[int]`. If we wanted `Parent.child` to be a nullable many-to-one, we can set both `Parent.child_id` and `Parent.child` to be `Optional[]`, in which case the configuration would look like:

    from typing import Optional

    class Parent(Base):
        __tablename__ = "parent_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        child_id: Mapped[Optional[int]] = mapped_column(ForeignKey("child_table.id"))
        child: Mapped[Optional["Child"]] = relationship(back_populates="parents")

    class Child(Base):
        __tablename__ = "child_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        parents: Mapped[List["Parent"]] = relationship(back_populates="child")

Above, the column for `Parent.child_id` will be created in DDL to allow `NULL` values. When using `mapped_column()` with explicit typing declarations, the specification of `child_id: Mapped[Optional[int]]` is equivalent to setting `Column.nullable` to `True` on the `Column`, whereas `child_id: Mapped[int]` is equivalent to setting it to `False`. See mapped_column() derives the datatype and nullability from the Mapped annotation for background on this behavior.

Tip

If using Python 3.10 or greater, PEP 604 syntax is more convenient to indicate optional types using `| None`, which when combined with PEP 563 postponed annotation evaluation so that string-quoted types aren’t required, would look like:

    from __future__ import annotations

    class Parent(Base):
        __tablename__ = "parent_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        child_id: Mapped[int | None] = mapped_column(ForeignKey("child_table.id"))
        child: Mapped[Child | None] = relationship(back_populates="parents")

    class Child(Base):
        __tablename__ = "child_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        parents: Mapped[List[Parent]] = relationship(back_populates="child")

## One To One

One To One is essentially a One To Many relationship from a foreign key perspective, but indicates that there will only be one row at any time that refers to a particular parent row.

When using annotated mappings with `Mapped`, the “one-to-one” convention is achieved by applying a non-collection type to the `Mapped` annotation on both sides of the relationship, which will imply to the ORM that a collection should not be used on either side, as in the example below:

    class Parent(Base):
        __tablename__ = "parent_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        child: Mapped["Child"] = relationship(back_populates="parent")

    class Child(Base):
        __tablename__ = "child_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        parent_id: Mapped[int] = mapped_column(ForeignKey("parent_table.id"))
        parent: Mapped["Parent"] = relationship(back_populates="child")

Above, when we load a `Parent` object, the `Parent.child` attribute will refer to a single `Child` object rather than a collection. If we replace the value of `Parent.child` with a new `Child` object, the ORM’s unit of work process will replace the previous `Child` row with the new one, setting the previous `child.parent_id` column to NULL by default unless there are specific cascade behaviors set up.

Tip

As mentioned previously, the ORM considers the “one-to-one” pattern as a convention, where it makes the assumption that when it loads the `Parent.child` attribute on a `Parent` object, it will get only one row back. If more than one row is returned, the ORM will emit a warning.

However, the `Child.parent` side of the above relationship remains as a “many-to-one” relationship. By itself, it will not detect assignment of more than one `Child`, unless the `relationship.single_parent` parameter is set, which may be useful:

    class Child(Base):
        __tablename__ = "child_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        parent_id: Mapped[int] = mapped_column(ForeignKey("parent_table.id"))
        parent: Mapped["Parent"] = relationship(back_populates="child", single_parent=True)

Outside of setting this parameter, the “one-to-many” side (which here is one-to-one by convention) will also not reliably detect if more than one `Child` is associated with a single `Parent`, such as in the case where the multiple `Child` objects are pending and not database-persistent.

Whether or not `relationship.single_parent` is used, it is recommended that the database schema include a unique constraint to indicate that the `Child.parent_id` column should be unique, to ensure at the database level that only one `Child` row may refer to a particular `Parent` row at a time (see Declarative Table Configuration for background on the `__table_args__` tuple syntax):

    from sqlalchemy import UniqueConstraint

    class Child(Base):
        __tablename__ = "child_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        parent_id: Mapped[int] = mapped_column(ForeignKey("parent_table.id"))
        parent: Mapped["Parent"] = relationship(back_populates="child")

        __table_args__ = (UniqueConstraint("parent_id"),)

New in version 2.0: The `relationship()` construct can derive the effective value of the `relationship.uselist` parameter from a given `Mapped` annotation.

### Setting uselist=False for non-annotated configurations

When using `relationship()` without the benefit of `Mapped` annotations, the one-to-one pattern can be enabled using the `relationship.uselist` parameter set to `False` on what would normally be the “many” side, illustrated in a non-annotated Declarative configuration below:

    class Parent(Base):
        __tablename__ = "parent_table"

        id = mapped_column(Integer, primary_key=True)
        child = relationship("Child", uselist=False, back_populates="parent")

    class Child(Base):
        __tablename__ = "child_table"

        id = mapped_column(Integer, primary_key=True)
        parent_id = mapped_column(ForeignKey("parent_table.id"))
        parent = relationship("Parent", back_populates="child")

## Many To Many

Many to Many adds an association table between two classes. The association table is nearly always given as a Core `Table` object or other Core selectable such as a `Join` object, and is indicated by the `relationship.secondary` argument to `relationship()`. Usually, the `Table` uses the `MetaData` object associated with the declarative base class, so that the `ForeignKey` directives can locate the remote tables with which to link:

    from __future__ import annotations

    from sqlalchemy import Column
    from sqlalchemy import Table
    from sqlalchemy import ForeignKey
    from sqlalchemy import Integer
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import relationship

    class Base(DeclarativeBase):
        pass

    # note for a Core table, we use the sqlalchemy.Column construct,
    # not sqlalchemy.orm.mapped_column
    association_table = Table(
        "association_table",
        Base.metadata,
        Column("left_id", ForeignKey("left_table.id")),
        Column("right_id", ForeignKey("right_table.id")),
    )

    class Parent(Base):
        __tablename__ = "left_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        children: Mapped[List[Child]] = relationship(secondary=association_table)

    class Child(Base):
        __tablename__ = "right_table"

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

Tip

The “association table” above has foreign key constraints established that refer to the two entity tables on either side of the relationship. The data type of each of `association.left_id` and `association.right_id` is normally inferred from that of the referenced table and may be omitted. It is also recommended, though not in any way required by SQLAlchemy, that the columns which refer to the two entity tables are established within either a unique constraint or more commonly as the primary key constraint; this ensures that duplicate rows won’t be persisted within the table regardless of issues on the application side:

    association_table = Table(
        "association_table",
        Base.metadata,
        Column("left_id", ForeignKey("left_table.id"), primary_key=True),
        Column("right_id", ForeignKey("right_table.id"), primary_key=True),
    )

### Setting Bi-Directional Many-to-many

For a bidirectional relationship, both sides of the relationship contain a collection. Specify using `relationship.back_populates`, and for each `relationship()` specify the common association table:

    from __future__ import annotations

    from sqlalchemy import Column
    from sqlalchemy import Table
    from sqlalchemy import ForeignKey
    from sqlalchemy import Integer
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import relationship

    class Base(DeclarativeBase):
        pass

    association_table = Table(
        "association_table",
        Base.metadata,
        Column("left_id", ForeignKey("left_table.id"), primary_key=True),
        Column("right_id", ForeignKey("right_table.id"), primary_key=True),
    )

    class Parent(Base):
        __tablename__ = "left_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        children: Mapped[List[Child]] = relationship(
            secondary=association_table, back_populates="parents"
        )

    class Child(Base):
        __tablename__ = "right_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        parents: Mapped[List[Parent]] = relationship(
            secondary=association_table, back_populates="children"
        )

### Using a late-evaluated form for the “secondary” argument

The `relationship.secondary` parameter of `relationship()` also accepts two different “late evaluated” forms, including string table name as well as lambda callable. See the section Using a late-evaluated form for the “secondary” argument of many-to-many for background and examples.

### Using Sets, Lists, or other Collection Types for Many To Many

Configuration of collections for a Many to Many relationship is identical to that of One To Many, as described at Using Sets, Lists, or other Collection Types for One To Many. For an annotated mapping using `Mapped`, the collection can be indicated by the type of collection used within the `Mapped` generic class, such as `set`:

    class Parent(Base):
        __tablename__ = "left_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        children: Mapped[Set["Child"]] = relationship(secondary=association_table)

When using non-annotated forms including imperative mappings, as is the case with one-to-many, the Python class to use as a collection may be passed using the `relationship.collection_class` parameter.

See also

Customizing Collection Access - contains further detail on collection configuration including some techniques to map `relationship()` to dictionaries.

### Deleting Rows from the Many to Many Table

A behavior which is unique to the `relationship.secondary` argument to `relationship()` is that the `Table` which is specified here is automatically subject to INSERT and DELETE statements, as objects are added or removed from the collection. There is no need to delete from this table manually. The act of removing a record from the collection will have the effect of the row being deleted on flush:

    # row will be deleted from the "secondary" table
    # automatically
    myparent.children.remove(somechild)

A question which often arises is how the row in the “secondary” table can be deleted when the child object is handed directly to `Session.delete()`:

    session.delete(somechild)

There are several possibilities here:

- If there is a `relationship()` from `Parent` to `Child`, but there is not a reverse-relationship that links a particular `Child` to each `Parent`, SQLAlchemy will not have any awareness that when deleting this particular `Child` object, it needs to maintain the “secondary” table that links it to the `Parent`. No delete of the “secondary” table will occur.

- If there is a relationship that links a particular `Child` to each `Parent`, suppose it’s called `Child.parents`, SQLAlchemy by default will load in the `Child.parents` collection to locate all `Parent` objects, and remove each row from the “secondary” table which establishes this link. Note that this relationship does not need to be bidirectional; SQLAlchemy is strictly looking at every `relationship()` associated with the `Child` object being deleted.

- A higher performing option here is to use ON DELETE CASCADE directives with the foreign keys used by the database. Assuming the database supports this feature, the database itself can be made to automatically delete rows in the “secondary” table as referencing rows in “child” are deleted. SQLAlchemy can be instructed to forego actively loading in the `Child.parents` collection in this case using the `relationship.passive_deletes` directive on `relationship()`; see Using foreign key ON DELETE cascade with ORM relationships for more details on this.

Note again, these behaviors are only relevant to the `relationship.secondary` option used with `relationship()`. If dealing with association tables that are mapped explicitly and are not present in the `relationship.secondary` option of a relevant `relationship()`, cascade rules can be used instead to automatically delete entities in reaction to a related entity being deleted - see Cascades for information on this feature.

See also

Using delete cascade with many-to-many relationships

Using foreign key ON DELETE with many-to-many relationships

## Association Object

The association object pattern is a variant on many-to-many: it’s used when an association table contains additional columns beyond those which are foreign keys to the parent and child (or left and right) tables, columns which are most ideally mapped to their own ORM mapped class. This mapped class is mapped against the `Table` that would otherwise be noted as `relationship.secondary` when using the many-to-many pattern.

In the association object pattern, the `relationship.secondary` parameter is not used; instead, a class is mapped directly to the association table. Two individual `relationship()` constructs then link first the parent side to the mapped association class via one to many, and then the mapped association class to the child side via many-to-one, to form a uni-directional association object relationship from parent, to association, to child. For a bi-directional relationship, four `relationship()` constructs are used to link the mapped association class to both parent and child in both directions.

The example below illustrates a new class `Association` which maps to the `Table` named `association`; this table now includes an additional column called `extra_data`, which is a string value that is stored along with each association between `Parent` and `Child`. By mapping the table to an explicit class, rudimental access from `Parent` to `Child` makes explicit use of `Association`:

    from typing import Optional

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

    class Base(DeclarativeBase):
        pass

    class Association(Base):
        __tablename__ = "association_table"
        left_id: Mapped[int] = mapped_column(ForeignKey("left_table.id"), primary_key=True)
        right_id: Mapped[int] = mapped_column(
            ForeignKey("right_table.id"), primary_key=True
        )
        extra_data: Mapped[Optional[str]]
        child: Mapped["Child"] = relationship()

    class Parent(Base):
        __tablename__ = "left_table"
        id: Mapped[int] = mapped_column(primary_key=True)
        children: Mapped[List["Association"]] = relationship()

    class Child(Base):
        __tablename__ = "right_table"
        id: Mapped[int] = mapped_column(primary_key=True)

To illustrate the bi-directional version, we add two more `relationship()` constructs, linked to the existing ones using `relationship.back_populates`:

    from typing import Optional

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

    class Base(DeclarativeBase):
        pass

    class Association(Base):
        __tablename__ = "association_table"
        left_id: Mapped[int] = mapped_column(ForeignKey("left_table.id"), primary_key=True)
        right_id: Mapped[int] = mapped_column(
            ForeignKey("right_table.id"), primary_key=True
        )
        extra_data: Mapped[Optional[str]]
        child: Mapped["Child"] = relationship(back_populates="parents")
        parent: Mapped["Parent"] = relationship(back_populates="children")

    class Parent(Base):
        __tablename__ = "left_table"
        id: Mapped[int] = mapped_column(primary_key=True)
        children: Mapped[List["Association"]] = relationship(back_populates="parent")

    class Child(Base):
        __tablename__ = "right_table"
        id: Mapped[int] = mapped_column(primary_key=True)
        parents: Mapped[List["Association"]] = relationship(back_populates="child")

Working with the association pattern in its direct form requires that child objects are associated with an association instance before being appended to the parent; similarly, access from parent to child goes through the association object:

    # create parent, append a child via association
    p = Parent()
    a = Association(extra_data="some data")
    a.child = Child()
    p.children.append(a)

    # iterate through child objects via association, including association
    # attributes
    for assoc in p.children:
        print(assoc.extra_data)
        print(assoc.child)

To enhance the association object pattern such that direct access to the `Association` object is optional, SQLAlchemy provides the Association Proxy extension. This extension allows the configuration of attributes which will access two “hops” with a single access, one “hop” to the associated object, and a second to a target attribute.

See also

Association Proxy - allows direct “many to many” style access between parent and child for a three-class association object mapping.

Warning

Avoid mixing the association object pattern with the many-to-many pattern directly, as this produces conditions where data may be read and written in an inconsistent fashion without special steps; the association proxy is typically used to provide more succinct access. For more detailed background on the caveats introduced by this combination, see the next section Combining Association Object with Many-to-Many Access Patterns.

### Combining Association Object with Many-to-Many Access Patterns

As mentioned in the previous section, the association object pattern does not automatically integrate with usage of the many-to-many pattern against the same tables/columns at the same time. From this it follows that read operations may return conflicting data and write operations may also attempt to flush conflicting changes, causing either integrity errors or unexpected inserts or deletes.

To illustrate, the example below configures a bidirectional many-to-many relationship between `Parent` and `Child` via `Parent.children` and `Child.parents`. At the same time, an association object relationship is also configured, between `Parent.child_associations -> Association.child` and `Child.parent_associations -> Association.parent`:

    from typing import Optional

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

    class Base(DeclarativeBase):
        pass

    class Association(Base):
        __tablename__ = "association_table"

        left_id: Mapped[int] = mapped_column(ForeignKey("left_table.id"), primary_key=True)
        right_id: Mapped[int] = mapped_column(
            ForeignKey("right_table.id"), primary_key=True
        )
        extra_data: Mapped[Optional[str]]

        # association between Assocation -> Child
        child: Mapped["Child"] = relationship(back_populates="parent_associations")

        # association between Assocation -> Parent
        parent: Mapped["Parent"] = relationship(back_populates="child_associations")

    class Parent(Base):
        __tablename__ = "left_table"

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

        # many-to-many relationship to Child, bypassing the `Association` class
        children: Mapped[List["Child"]] = relationship(
            secondary="association_table", back_populates="parents"
        )

        # association between Parent -> Association -> Child
        child_associations: Mapped[List["Association"]] = relationship(
            back_populates="parent"
        )

    class Child(Base):
        __tablename__ = "right_table"

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

        # many-to-many relationship to Parent, bypassing the `Association` class
        parents: Mapped[List["Parent"]] = relationship(
            secondary="association_table", back_populates="children"
        )

        # association between Child -> Association -> Parent
        parent_associations: Mapped[List["Association"]] = relationship(
            back_populates="child"
        )

When using this ORM model to make changes, changes made to `Parent.children` will not be coordinated with changes made to `Parent.child_associations` or `Child.parent_associations` in Python; while all of these relationships will continue to function normally by themselves, changes on one will not show up in another until the `Session` is expired, which normally occurs automatically after `Session.commit()`.

Additionally, if conflicting changes are made, such as adding a new `Association` object while also appending the same related `Child` to `Parent.children`, this will raise integrity errors when the unit of work flush process proceeds, as in the example below:

    p1 = Parent()
    c1 = Child()
    p1.children.append(c1)

    # redundant, will cause a duplicate INSERT on Association
    p1.child_associations.append(Association(child=c1))

Appending `Child` to `Parent.children` directly also implies the creation of rows in the `association` table without indicating any value for the `association.extra_data` column, which will receive `NULL` for its value.

It’s fine to use a mapping like the above if you know what you’re doing; there may be good reason to use many-to-many relationships in the case where use of the “association object” pattern is infrequent, which is that it’s easier to load relationships along a single many-to-many relationship, which can also optimize slightly better how the “secondary” table is used in SQL statements, compared to how two separate relationships to an explicit association class is used. It’s at least a good idea to apply the `relationship.viewonly` parameter to the “secondary” relationship to avoid the issue of conflicting changes occurring, as well as preventing `NULL` being written to the additional association columns, as below:

    class Parent(Base):
        __tablename__ = "left_table"

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

        # many-to-many relationship to Child, bypassing the `Association` class
        children: Mapped[List["Child"]] = relationship(
            secondary="association_table", back_populates="parents", viewonly=True
        )

        # association between Parent -> Association -> Child
        child_associations: Mapped[List["Association"]] = relationship(
            back_populates="parent"
        )

    class Child(Base):
        __tablename__ = "right_table"

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

        # many-to-many relationship to Parent, bypassing the `Association` class
        parents: Mapped[List["Parent"]] = relationship(
            secondary="association_table", back_populates="children", viewonly=True
        )

        # association between Child -> Association -> Parent
        parent_associations: Mapped[List["Association"]] = relationship(
            back_populates="child"
        )

The above mapping will not write any changes to `Parent.children` or `Child.parents` to the database, preventing conflicting writes. However, reads of `Parent.children` or `Child.parents` will not necessarily match the data that’s read from `Parent.child_associations` or `Child.parent_associations`, if changes are being made to these collections within the same transaction or `Session` as where the viewonly collections are being read. If use of the association object relationships is infrequent and is carefully organized against code that accesses the many-to-many collections to avoid stale reads (in extreme cases, making direct use of `Session.expire()` to cause collections to be refreshed within the current transaction), the pattern may be feasible.

A popular alternative to the above pattern is one where the direct many-to-many `Parent.children` and `Child.parents` relationships are replaced with an extension that will transparently proxy through the `Association` class, while keeping everything consistent from the ORM’s point of view. This extension is known as the Association Proxy.

See also

Association Proxy - allows direct “many to many” style access between parent and child for a three-class association object mapping.

## Late-Evaluation of Relationship Arguments

Most of the examples in the preceding sections illustrate mappings where the various `relationship()` constructs refer to their target classes using a string name, rather than the class itself, such as when using `Mapped`, a forward reference is generated that exists at runtime only as a string:

    class Parent(Base):
        # ...

        children: Mapped[List["Child"]] = relationship(back_populates="parent")

    class Child(Base):
        # ...

        parent: Mapped["Parent"] = relationship(back_populates="children")

Similarly, when using non-annotated forms such as non-annotated Declarative or Imperative mappings, a string name is also supported directly by the `relationship()` construct:

    registry.map_imperatively(
        Parent,
        parent_table,
        properties={"children": relationship("Child", back_populates="parent")},
    )

    registry.map_imperatively(
        Child,
        child_table,
        properties={"parent": relationship("Parent", back_populates="children")},
    )

These string names are resolved into classes in the mapper resolution stage, which is an internal process that occurs typically after all mappings have been defined and is normally triggered by the first usage of the mappings themselves. The `registry` object is the container where these names are stored and resolved to the mapped classes to which they refer.

In addition to the main class argument for `relationship()`, other arguments which depend upon the columns present on an as-yet undefined class may also be specified either as Python functions, or more commonly as strings. For most of these arguments except that of the main argument, string inputs are evaluated as Python expressions using Python’s built-in eval() function, as they are intended to receive complete SQL expressions.

Warning

As the Python `eval()` function is used to interpret the late-evaluated string arguments passed to `relationship()` mapper configuration construct, these arguments should not be repurposed such that they would receive untrusted user input; `eval()` is not secure against untrusted user input.

The full namespace available within this evaluation includes all classes mapped for this declarative base, as well as the contents of the `sqlalchemy` package, including expression functions like `desc()` and `sqlalchemy.sql.functions.func`:

    class Parent(Base):
        # ...

        children: Mapped[List["Child"]] = relationship(
            order_by="desc(Child.email_address)",
            primaryjoin="Parent.id == Child.parent_id",
        )

For the case where more than one module contains a class of the same name, string class names can also be specified as module-qualified paths within any of these string expressions:

    class Parent(Base):
        # ...

        children: Mapped[List["myapp.mymodel.Child"]] = relationship(
            order_by="desc(myapp.mymodel.Child.email_address)",
            primaryjoin="myapp.mymodel.Parent.id == myapp.mymodel.Child.parent_id",
        )

In an example like the above, the string passed to `Mapped` can be disambiguated from a specific class argument by passing the class location string directly to `relationship.argument` as well. Below illustrates a typing-only import for `Child`, combined with a runtime specifier for the target class that will search for the correct name within the `registry`:

    import typing

    if typing.TYPE_CHECKING:
        from myapp.mymodel import Child

    class Parent(Base):
        # ...

        children: Mapped[List["Child"]] = relationship(
            "myapp.mymodel.Child",
            order_by="desc(myapp.mymodel.Child.email_address)",
            primaryjoin="myapp.mymodel.Parent.id == myapp.mymodel.Child.parent_id",
        )

The qualified path can be any partial path that removes ambiguity between the names. For example, to disambiguate between `myapp.model1.Child` and `myapp.model2.Child`, we can specify `model1.Child` or `model2.Child`:

    class Parent(Base):
        # ...

        children: Mapped[List["Child"]] = relationship(
            "model1.Child",
            order_by="desc(mymodel1.Child.email_address)",
            primaryjoin="Parent.id == model1.Child.parent_id",
        )

The `relationship()` construct also accepts Python functions or lambdas as input for these arguments. A Python functional approach might look like the following:

    import typing

    from sqlalchemy import desc

    if typing.TYPE_CHECKING:
        from myapplication import Child

    def _resolve_child_model():
        from myapplication import Child

        return Child

    class Parent(Base):
        # ...

        children: Mapped[List["Child"]] = relationship(
            _resolve_child_model,
            order_by=lambda: desc(_resolve_child_model().email_address),
            primaryjoin=lambda: Parent.id == _resolve_child_model().parent_id,
        )

The full list of parameters which accept Python functions/lambdas or strings that will be passed to `eval()` are:

- `relationship.order_by`

- `relationship.primaryjoin`

- `relationship.secondaryjoin`

- `relationship.secondary`

- `relationship.remote_side`

- `relationship.foreign_keys`

- `relationship._user_defined_foreign_keys`

Warning

As stated previously, the above parameters to `relationship()` are evaluated as Python code expressions using eval(). DO NOT PASS UNTRUSTED INPUT TO THESE ARGUMENTS.

### Adding Relationships to Mapped Classes After Declaration

It should also be noted that in a similar way as described at Appending additional columns to an existing Declarative mapped class, any `MapperProperty` construct can be added to a declarative base mapping at any time (noting that annotated forms are not supported in this context). If we wanted to implement this `relationship()` after the `Address` class were available, we could also apply it afterwards:

    # first, module A, where Child has not been created yet,
    # we create a Parent class which knows nothing about Child

    class Parent(Base): ...

    # ... later, in Module B, which is imported after module A:

    class Child(Base): ...

    from module_a import Parent

    # assign the User.addresses relationship as a class variable.  The
    # declarative base class will intercept this and map the relationship.
    Parent.children = relationship(Child, primaryjoin=Child.parent_id == Parent.id)

As is the case for ORM mapped columns, there’s no capability for the `Mapped` annotation type to take part in this operation; therefore, the related class must be specified directly within the `relationship()` construct, either as the class itself, the string name of the class, or a callable function that returns a reference to the target class.

Note

As is the case for ORM mapped columns, assignment of mapped properties to an already mapped class will only function correctly if the “declarative base” class is used, meaning the user-defined subclass of `DeclarativeBase` or the dynamically generated class returned by `declarative_base()` or `registry.generate_base()`. This “base” class includes a Python metaclass which implements a special `__setattr__()` method that intercepts these operations.

Runtime assignment of class-mapped attributes to a mapped class will not work if the class is mapped using decorators like `registry.mapped()` or imperative functions like `registry.map_imperatively()`.

### Using a late-evaluated form for the “secondary” argument of many-to-many

Many-to-many relationships make use of the `relationship.secondary` parameter, which ordinarily indicates a reference to a typically non-mapped `Table` object or other Core selectable object. Late evaluation using a lambda callable is typical.

For the example given at Many To Many, if we assumed that the `association_table` `Table` object would be defined at a point later on in the module than the mapped class itself, we may write the `relationship()` using a lambda as:

    class Parent(Base):
        __tablename__ = "left_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        children: Mapped[List["Child"]] = relationship(
            "Child", secondary=lambda: association_table
        )

As a shortcut for table names that are also valid Python identifiers, the `relationship.secondary` parameter may also be passed as a string, where resolution works by evaluation of the string as a Python expression, with simple identifier names linked to same-named `Table` objects that are present in the same `MetaData` collection referenced by the current `registry`.

In the example below, the expression `"association_table"` is evaluated as a variable named “association_table” that is resolved against the table names within the `MetaData` collection:

    class Parent(Base):
        __tablename__ = "left_table"

        id: Mapped[int] = mapped_column(primary_key=True)
        children: Mapped[List["Child"]] = relationship(secondary="association_table")

Note

When passed as a string, the name passed to `relationship.secondary` must be a valid Python identifier starting with a letter and containing only alphanumeric characters or underscores. Other characters such as dashes etc. will be interpreted as Python operators which will not resolve to the name given. Please consider using lambda expressions rather than strings for improved clarity.

Warning

When passed as a string, `relationship.secondary` argument is interpreted using Python’s `eval()` function, even though it’s typically the name of a table. DO NOT PASS UNTRUSTED INPUT TO THIS STRING.