SQLAlchemy 1.1 Documentation
Linking Relationships with Backref¶
The backref
keyword argument was first introduced in Object Relational Tutorial, and has been mentioned throughout many of the examples here. What does it actually do ? Let’s start with the canonical User
and Address
scenario:
from sqlalchemy import Integer, ForeignKey, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship
Base = declarative_base()
class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
name = Column(String)
addresses = relationship("Address", backref="user")
class Address(Base):
__tablename__ = 'address'
id = Column(Integer, primary_key=True)
email = Column(String)
user_id = Column(Integer, ForeignKey('user.id'))
The above configuration establishes a collection of Address
objects on User
called User.addresses
. It also establishes a .user
attribute on Address
which will refer to the parent User
object.
In fact, the backref
keyword is only a common shortcut for placing a second relationship()
onto the Address
mapping, including the establishment of an event listener on both sides which will mirror attribute operations in both directions. The above configuration is equivalent to:
from sqlalchemy import Integer, ForeignKey, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship
Base = declarative_base()
class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
name = Column(String)
addresses = relationship("Address", back_populates="user")
class Address(Base):
__tablename__ = 'address'
id = Column(Integer, primary_key=True)
email = Column(String)
user_id = Column(Integer, ForeignKey('user.id'))
user = relationship("User", back_populates="addresses")
Above, we add a .user
relationship to Address
explicitly. On both relationships, the back_populates
directive tells each relationship about the other one, indicating that they should establish “bidirectional” behavior between each other. The primary effect of this configuration is that the relationship adds event handlers to both attributes which have the behavior of “when an append or set event occurs here, set ourselves onto the incoming attribute using this particular attribute name”. The behavior is illustrated as follows. Start with a User
and an Address
instance. The .addresses
collection is empty, and the .user
attribute is None
:
>>> u1 = User()
>>> a1 = Address()
>>> u1.addresses
[]
>>> print(a1.user)
None
However, once the Address
is appended to the u1.addresses
collection, both the collection and the scalar attribute have been populated:
>>> u1.addresses.append(a1)
>>> u1.addresses
[<__main__.Address object at 0x12a6ed0>]
>>> a1.user
<__main__.User object at 0x12a6590>
This behavior of course works in reverse for removal operations as well, as well as for equivalent operations on both sides. Such as when .user
is set again to None
, the Address
object is removed from the reverse collection:
>>> a1.user = None
>>> u1.addresses
[]
The manipulation of the .addresses
collection and the .user
attribute occurs entirely in Python without any interaction with the SQL database. Without this behavior, the proper state would be apparent on both sides once the data has been flushed to the database, and later reloaded after a commit or expiration operation occurs. The backref
/back_populates
behavior has the advantage that common bidirectional operations can reflect the correct state without requiring a database round trip.
Remember, when the backref
keyword is used on a single relationship, it’s exactly the same as if the above two relationships were created individually using back_populates
on each.
Backref Arguments¶
We’ve established that the backref
keyword is merely a shortcut for building two individual relationship()
constructs that refer to each other. Part of the behavior of this shortcut is that certain configurational arguments applied to the relationship()
will also be applied to the other direction - namely those arguments that describe the relationship at a schema level, and are unlikely to be different in the reverse direction. The usual case here is a many-to-many relationship()
that has a secondary
argument, or a one-to-many or many-to-one which has a primaryjoin
argument (the primaryjoin
argument is discussed in Specifying Alternate Join Conditions). Such as if we limited the list of Address
objects to those which start with “tony”:
from sqlalchemy import Integer, ForeignKey, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship
Base = declarative_base()
class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
name = Column(String)
addresses = relationship("Address",
primaryjoin="and_(User.id==Address.user_id, "
"Address.email.startswith('tony'))",
backref="user")
class Address(Base):
__tablename__ = 'address'
id = Column(Integer, primary_key=True)
email = Column(String)
user_id = Column(Integer, ForeignKey('user.id'))
We can observe, by inspecting the resulting property, that both sides of the relationship have this join condition applied:
>>> print(User.addresses.property.primaryjoin)
"user".id = address.user_id AND address.email LIKE :email_1 || '%%'
>>>
>>> print(Address.user.property.primaryjoin)
"user".id = address.user_id AND address.email LIKE :email_1 || '%%'
>>>
This reuse of arguments should pretty much do the “right thing” - it uses only arguments that are applicable, and in the case of a many-to- many relationship, will reverse the usage of primaryjoin
and secondaryjoin
to correspond to the other direction (see the example in Self-Referential Many-to-Many Relationship for this).
It’s very often the case however that we’d like to specify arguments that are specific to just the side where we happened to place the “backref”. This includes relationship()
arguments like lazy
, remote_side
, cascade
and cascade_backrefs
. For this case we use the backref()
function in place of a string:
# <other imports>
from sqlalchemy.orm import backref
class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
name = Column(String)
addresses = relationship("Address",
backref=backref("user", lazy="joined"))
Where above, we placed a lazy="joined"
directive only on the Address.user
side, indicating that when a query against Address
is made, a join to the User
entity should be made automatically which will populate the .user
attribute of each returned Address
. The backref()
function formatted the arguments we gave it into a form that is interpreted by the receiving relationship()
as additional arguments to be applied to the new relationship it creates.
One Way Backrefs¶
An unusual case is that of the “one way backref”. This is where the “back-populating” behavior of the backref is only desirable in one direction. An example of this is a collection which contains a filtering primaryjoin
condition. We’d like to append items to this collection as needed, and have them populate the “parent” object on the incoming object. However, we’d also like to have items that are not part of the collection, but still have the same “parent” association - these items should never be in the collection.
Taking our previous example, where we established a primaryjoin
that limited the collection only to Address
objects whose email address started with the word tony
, the usual backref behavior is that all items populate in both directions. We wouldn’t want this behavior for a case like the following:
>>> u1 = User()
>>> a1 = Address(email='mary')
>>> a1.user = u1
>>> u1.addresses
[<__main__.Address object at 0x1411910>]
Above, the Address
object that doesn’t match the criterion of “starts with ‘tony’” is present in the addresses
collection of u1
. After these objects are flushed, the transaction committed and their attributes expired for a re-load, the addresses
collection will hit the database on next access and no longer have this Address
object present, due to the filtering condition. But we can do away with this unwanted side of the “backref” behavior on the Python side by using two separate relationship()
constructs, placing back_populates
only on one side:
from sqlalchemy import Integer, ForeignKey, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship
Base = declarative_base()
class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
name = Column(String)
addresses = relationship("Address",
primaryjoin="and_(User.id==Address.user_id, "
"Address.email.startswith('tony'))",
back_populates="user")
class Address(Base):
__tablename__ = 'address'
id = Column(Integer, primary_key=True)
email = Column(String)
user_id = Column(Integer, ForeignKey('user.id'))
user = relationship("User")
With the above scenario, appending an Address
object to the .addresses
collection of a User
will always establish the .user
attribute on that Address
:
>>> u1 = User()
>>> a1 = Address(email='tony')
>>> u1.addresses.append(a1)
>>> a1.user
<__main__.User object at 0x1411850>
However, applying a User
to the .user
attribute of an Address
, will not append the Address
object to the collection:
>>> a2 = Address(email='mary')
>>> a2.user = u1
>>> a2 in u1.addresses
False
Of course, we’ve disabled some of the usefulness of backref
here, in that when we do append an Address
that corresponds to the criteria of email.startswith('tony')
, it won’t show up in the User.addresses
collection until the session is flushed, and the attributes reloaded after a commit or expire operation. While we could consider an attribute event that checks this criterion in Python, this starts to cross the line of duplicating too much SQL behavior in Python. The backref behavior itself is only a slight transgression of this philosophy - SQLAlchemy tries to keep these to a minimum overall.