SQLAlchemy 2.0 Documentation
SQLAlchemy ORM
- ORM Quick Start
- ORM Mapped Class Configuration
- Relationship Configuration
- Basic Relationship Patterns¶
- Adjacency List Relationships
- Configuring how Relationship Joins
- Working with Large Collections
- Collection Customization and API Details
- Special Relationship Persistence Patterns
- Using the legacy ‘backref’ relationship parameter
- Relationships API
- ORM Querying Guide
- Using the Session
- Events and Internals
- ORM Extensions
- ORM Examples
Project Versions
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.
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()
fromParent
toChild
, but there is not a reverse-relationship that links a particularChild
to eachParent
, SQLAlchemy will not have any awareness that when deleting this particularChild
object, it needs to maintain the “secondary” table that links it to theParent
. No delete of the “secondary” table will occur.If there is a relationship that links a particular
Child
to eachParent
, suppose it’s calledChild.parents
, SQLAlchemy by default will load in theChild.parents
collection to locate allParent
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 everyrelationship()
associated with theChild
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 therelationship.passive_deletes
directive onrelationship()
; 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.
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:
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.
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