Association Proxy — SQLAlchemy 2.0.0b1 documentation

Association Proxy

associationproxy is used to create a read/write view of a target attribute across a relationship. It essentially conceals the usage of a “middle” attribute between two endpoints, and can be used to cherry-pick fields from a collection of related objects or to reduce the verbosity of using the association object pattern. Applied creatively, the association proxy allows the construction of sophisticated collections and dictionary views of virtually any geometry, persisted to the database using standard, transparently configured relational patterns.

Simplifying Scalar Collections

Consider a many-to-many mapping between two classes, User and Keyword. Each User can have any number of Keyword objects, and vice-versa (the many-to-many pattern is described at Many To Many):

from sqlalchemy import Column, Integer, String, ForeignKey, Table
from sqlalchemy.orm import declarative_base, relationship

Base = declarative_base()

class User(Base):
    __tablename__ = 'user'
    id = Column(Integer, primary_key=True)
    name = Column(String(64))
    kw = relationship("Keyword", secondary=lambda: userkeywords_table)

    def __init__(self, name):
        self.name = name

class Keyword(Base):
    __tablename__ = 'keyword'
    id = Column(Integer, primary_key=True)
    keyword = Column('keyword', String(64))

    def __init__(self, keyword):
        self.keyword = keyword

userkeywords_table = Table('userkeywords', Base.metadata,
    Column('user_id', Integer, ForeignKey("user.id"),
           primary_key=True),
    Column('keyword_id', Integer, ForeignKey("keyword.id"),
           primary_key=True)
)

Reading and manipulating the collection of “keyword” strings associated with User requires traversal from each collection element to the .keyword attribute, which can be awkward:

>>> user = User('jek')
>>> user.kw.append(Keyword('cheese inspector'))
>>> print(user.kw)
[<__main__.Keyword object at 0x12bf830>]
>>> print(user.kw[0].keyword)
cheese inspector
>>> print([keyword.keyword for keyword in user.kw])
['cheese inspector']

The association_proxy is applied to the User class to produce a “view” of the kw relationship, which only exposes the string value of .keyword associated with each Keyword object:

from sqlalchemy.ext.associationproxy import association_proxy

class User(Base):
    __tablename__ = 'user'
    id = Column(Integer, primary_key=True)
    name = Column(String(64))
    kw = relationship("Keyword", secondary=lambda: userkeywords_table)

    def __init__(self, name):
        self.name = name

    # proxy the 'keyword' attribute from the 'kw' relationship
    keywords = association_proxy('kw', 'keyword')

We can now reference the .keywords collection as a listing of strings, which is both readable and writable. New Keyword objects are created for us transparently:

>>> user = User('jek')
>>> user.keywords.append('cheese inspector')
>>> user.keywords
['cheese inspector']
>>> user.keywords.append('snack ninja')
>>> user.kw
[<__main__.Keyword object at 0x12cdd30>, <__main__.Keyword object at 0x12cde30>]

The AssociationProxy object produced by the association_proxy() function is an instance of a Python descriptor. It is always declared with the user-defined class being mapped, regardless of whether Declarative or classical mappings via the mapper() function are used.

The proxy functions by operating upon the underlying mapped attribute or collection in response to operations, and changes made via the proxy are immediately apparent in the mapped attribute, as well as vice versa. The underlying attribute remains fully accessible.

When first accessed, the association proxy performs introspection operations on the target collection so that its behavior corresponds correctly. Details such as if the locally proxied attribute is a collection (as is typical) or a scalar reference, as well as if the collection acts like a set, list, or dictionary is taken into account, so that the proxy should act just like the underlying collection or attribute does.

Creation of New Values

When a list append() event (or set add(), dictionary __setitem__(), or scalar assignment event) is intercepted by the association proxy, it instantiates a new instance of the “intermediary” object using its constructor, passing as a single argument the given value. In our example above, an operation like:

user.keywords.append('cheese inspector')

Is translated by the association proxy into the operation:

user.kw.append(Keyword('cheese inspector'))

The example works here because we have designed the constructor for Keyword to accept a single positional argument, keyword. For those cases where a single-argument constructor isn’t feasible, the association proxy’s creational behavior can be customized using the creator argument, which references a callable (i.e. Python function) that will produce a new object instance given the singular argument. Below we illustrate this using a lambda as is typical:

class User(Base):
    # ...

    # use Keyword(keyword=kw) on append() events
    keywords = association_proxy('kw', 'keyword',
                    creator=lambda kw: Keyword(keyword=kw))

The creator function accepts a single argument in the case of a list- or set- based collection, or a scalar attribute. In the case of a dictionary-based collection, it accepts two arguments, “key” and “value”. An example of this is below in Proxying to Dictionary Based Collections.

Simplifying Association Objects

The “association object” pattern is an extended form of a many-to-many relationship, and is described at Association Object. Association proxies are useful for keeping “association objects” out of the way during regular use.

Suppose our userkeywords table above had additional columns which we’d like to map explicitly, but in most cases we don’t require direct access to these attributes. Below, we illustrate a new mapping which introduces the UserKeyword class, which is mapped to the userkeywords table illustrated earlier. This class adds an additional column special_key, a value which we occasionally want to access, but not in the usual case. We create an association proxy on the User class called keywords, which will bridge the gap from the user_keywords collection of User to the .keyword attribute present on each UserKeyword:

from sqlalchemy import Column, Integer, String, ForeignKey
from sqlalchemy.ext.associationproxy import association_proxy
from sqlalchemy.orm import backref, declarative_base, relationship

Base = declarative_base()

class User(Base):
    __tablename__ = 'user'
    id = Column(Integer, primary_key=True)
    name = Column(String(64))

    # association proxy of "user_keywords" collection
    # to "keyword" attribute
    keywords = association_proxy('user_keywords', 'keyword')

    def __init__(self, name):
        self.name = name

class UserKeyword(Base):
    __tablename__ = 'user_keyword'
    user_id = Column(Integer, ForeignKey('user.id'), primary_key=True)
    keyword_id = Column(Integer, ForeignKey('keyword.id'), primary_key=True)
    special_key = Column(String(50))

    # bidirectional attribute/collection of "user"/"user_keywords"
    user = relationship(User,
                backref=backref("user_keywords",
                                cascade="all, delete-orphan")
            )

    # reference to the "Keyword" object
    keyword = relationship("Keyword")

    def __init__(self, keyword=None, user=None, special_key=None):
        self.user = user
        self.keyword = keyword
        self.special_key = special_key

class Keyword(Base):
    __tablename__ = 'keyword'
    id = Column(Integer, primary_key=True)
    keyword = Column('keyword', String(64))

    def __init__(self, keyword):
        self.keyword = keyword

    def __repr__(self):
        return 'Keyword(%s)' % repr(self.keyword)

With the above configuration, we can operate upon the .keywords collection of each User object, and the usage of UserKeyword is concealed:

>>> user = User('log')
>>> for kw in (Keyword('new_from_blammo'), Keyword('its_big')):
...     user.keywords.append(kw)
...
>>> print(user.keywords)
[Keyword('new_from_blammo'), Keyword('its_big')]

Where above, each .keywords.append() operation is equivalent to:

>>> user.user_keywords.append(UserKeyword(Keyword('its_heavy')))

The UserKeyword association object has two attributes here which are populated; the .keyword attribute is populated directly as a result of passing the Keyword object as the first argument. The .user argument is then assigned as the UserKeyword object is appended to the User.user_keywords collection, where the bidirectional relationship configured between User.user_keywords and UserKeyword.user results in a population of the UserKeyword.user attribute. The special_key argument above is left at its default value of None.

For those cases where we do want special_key to have a value, we create the UserKeyword object explicitly. Below we assign all three attributes, where the assignment of .user has the effect of the UserKeyword being appended to the User.user_keywords collection:

>>> UserKeyword(Keyword('its_wood'), user, special_key='my special key')

The association proxy returns to us a collection of Keyword objects represented by all these operations:

>>> user.keywords
[Keyword('new_from_blammo'), Keyword('its_big'), Keyword('its_heavy'), Keyword('its_wood')]

Proxying to Dictionary Based Collections

The association proxy can proxy to dictionary based collections as well. SQLAlchemy mappings usually use the attribute_mapped_collection() collection type to create dictionary collections, as well as the extended techniques described in Custom Dictionary-Based Collections.

The association proxy adjusts its behavior when it detects the usage of a dictionary-based collection. When new values are added to the dictionary, the association proxy instantiates the intermediary object by passing two arguments to the creation function instead of one, the key and the value. As always, this creation function defaults to the constructor of the intermediary class, and can be customized using the creator argument.

Below, we modify our UserKeyword example such that the User.user_keywords collection will now be mapped using a dictionary, where the UserKeyword.special_key argument will be used as the key for the dictionary. We then apply a creator argument to the User.keywords proxy so that these values are assigned appropriately when new elements are added to the dictionary:

from sqlalchemy import Column, Integer, String, ForeignKey
from sqlalchemy.ext.associationproxy import association_proxy
from sqlalchemy.orm import backref, declarative_base, relationship
from sqlalchemy.orm.collections import attribute_mapped_collection

Base = declarative_base()

class User(Base):
    __tablename__ = 'user'
    id = Column(Integer, primary_key=True)
    name = Column(String(64))

    # proxy to 'user_keywords', instantiating UserKeyword
    # assigning the new key to 'special_key', values to
    # 'keyword'.
    keywords = association_proxy('user_keywords', 'keyword',
                    creator=lambda k, v:
                                UserKeyword(special_key=k, keyword=v)
                )

    def __init__(self, name):
        self.name = name

class UserKeyword(Base):
    __tablename__ = 'user_keyword'
    user_id = Column(Integer, ForeignKey('user.id'), primary_key=True)
    keyword_id = Column(Integer, ForeignKey('keyword.id'), primary_key=True)
    special_key = Column(String)

    # bidirectional user/user_keywords relationships, mapping
    # user_keywords with a dictionary against "special_key" as key.
    user = relationship(User, backref=backref(
                    "user_keywords",
                    collection_class=attribute_mapped_collection("special_key"),
                    cascade="all, delete-orphan"
                    )
                )
    keyword = relationship("Keyword")

class Keyword(Base):
    __tablename__ = 'keyword'
    id = Column(Integer, primary_key=True)
    keyword = Column('keyword', String(64))

    def __init__(self, keyword):
        self.keyword = keyword

    def __repr__(self):
        return 'Keyword(%s)' % repr(self.keyword)

We illustrate the .keywords collection as a dictionary, mapping the UserKeyword.special_key value to Keyword objects:

>>> user = User('log')

>>> user.keywords['sk1'] = Keyword('kw1')
>>> user.keywords['sk2'] = Keyword('kw2')

>>> print(user.keywords)
{'sk1': Keyword('kw1'), 'sk2': Keyword('kw2')}

Composite Association Proxies

Given our previous examples of proxying from relationship to scalar attribute, proxying across an association object, and proxying dictionaries, we can combine all three techniques together to give User a keywords dictionary that deals strictly with the string value of special_key mapped to the string keyword. Both the UserKeyword and Keyword classes are entirely concealed. This is achieved by building an association proxy on User that refers to an association proxy present on UserKeyword:

from sqlalchemy import Column, Integer, String, ForeignKey
from sqlalchemy.ext.associationproxy import association_proxy
from sqlalchemy.orm import backref, declarative_base, relationship
from sqlalchemy.orm.collections import attribute_mapped_collection

Base = declarative_base()

class User(Base):
    __tablename__ = 'user'
    id = Column(Integer, primary_key=True)
    name = Column(String(64))

    # the same 'user_keywords'->'keyword' proxy as in
    # the basic dictionary example.
    keywords = association_proxy(
        'user_keywords',
        'keyword',
        creator=lambda k, v: UserKeyword(special_key=k, keyword=v)
    )

    # another proxy that is directly column-targeted
    special_keys = association_proxy("user_keywords", "special_key")

    def __init__(self, name):
        self.name = name

class UserKeyword(Base):
    __tablename__ = 'user_keyword'
    user_id = Column(ForeignKey('user.id'), primary_key=True)
    keyword_id = Column(ForeignKey('keyword.id'), primary_key=True)
    special_key = Column(String)
    user = relationship(
        User,
        backref=backref(
            "user_keywords",
            collection_class=attribute_mapped_collection("special_key"),
            cascade="all, delete-orphan"
        )
    )

    # the relationship to Keyword is now called
    # 'kw'
    kw = relationship("Keyword")

    # 'keyword' is changed to be a proxy to the
    # 'keyword' attribute of 'Keyword'
    keyword = association_proxy('kw', 'keyword')

class Keyword(Base):
    __tablename__ = 'keyword'
    id = Column(Integer, primary_key=True)
    keyword = Column('keyword', String(64))

    def __init__(self, keyword):
        self.keyword = keyword

User.keywords is now a dictionary of string to string, where UserKeyword and Keyword objects are created and removed for us transparently using the association proxy. In the example below, we illustrate usage of the assignment operator, also appropriately handled by the association proxy, to apply a dictionary value to the collection at once:

>>> user = User('log')
>>> user.keywords = {
...     'sk1':'kw1',
...     'sk2':'kw2'
... }
>>> print(user.keywords)
{'sk1': 'kw1', 'sk2': 'kw2'}

>>> user.keywords['sk3'] = 'kw3'
>>> del user.keywords['sk2']
>>> print(user.keywords)
{'sk1': 'kw1', 'sk3': 'kw3'}

>>> # illustrate un-proxied usage
... print(user.user_keywords['sk3'].kw)
<__main__.Keyword object at 0x12ceb90>

One caveat with our example above is that because Keyword objects are created for each dictionary set operation, the example fails to maintain uniqueness for the Keyword objects on their string name, which is a typical requirement for a tagging scenario such as this one. For this use case the recipe UniqueObject, or a comparable creational strategy, is recommended, which will apply a “lookup first, then create” strategy to the constructor of the Keyword class, so that an already existing Keyword is returned if the given name is already present.

Querying with Association Proxies

The AssociationProxy features simple SQL construction capabilities which work at the class level in a similar way as other ORM-mapped attributes. Class-bound attributes such as User.keywords and User.special_keys in the preceding example will provide for a SQL generating construct when accessed at the class level.

Note

The primary purpose of the association proxy extension is to allow for improved persistence and object-access patterns with mapped object instances that are already loaded. The class-bound querying feature is of limited use and will not replace the need to refer to the underlying attributes when constructing SQL queries with JOINs, eager loading options, etc.

The SQL generated takes the form of a correlated subquery against the EXISTS SQL operator so that it can be used in a WHERE clause without the need for additional modifications to the enclosing query. If the immediate target of an association proxy is a mapped column expression, standard column operators can be used which will be embedded in the subquery. For example a straight equality operator:

>>> print(session.query(User).filter(User.special_keys == "jek"))
SELECT "user".id AS user_id, "user".name AS user_name
FROM "user"
WHERE EXISTS (SELECT 1
FROM user_keyword
WHERE "user".id = user_keyword.user_id AND user_keyword.special_key = :special_key_1)

a LIKE operator:

>>> print(session.query(User).filter(User.special_keys.like("%jek")))
SELECT "user".id AS user_id, "user".name AS user_name
FROM "user"
WHERE EXISTS (SELECT 1
FROM user_keyword
WHERE "user".id = user_keyword.user_id AND user_keyword.special_key LIKE :special_key_1)

For association proxies where the immediate target is a related object or collection, or another association proxy or attribute on the related object, relationship-oriented operators can be used instead, such as _orm.PropComparator.has() and _orm.PropComparator.any(). The User.keywords attribute is in fact two association proxies linked together, so when using this proxy for generating SQL phrases, we get two levels of EXISTS subqueries:

>>> print(session.query(User).filter(User.keywords.any(Keyword.keyword == "jek")))
SELECT "user".id AS user_id, "user".name AS user_name
FROM "user"
WHERE EXISTS (SELECT 1
FROM user_keyword
WHERE "user".id = user_keyword.user_id AND (EXISTS (SELECT 1
FROM keyword
WHERE keyword.id = user_keyword.keyword_id AND keyword.keyword = :keyword_1)))

This is not the most efficient form of SQL, so while association proxies can be convenient for generating WHERE criteria quickly, SQL results should be inspected and “unrolled” into explicit JOIN criteria for best use, especially when chaining association proxies together.

Changed in version 1.3: Association proxy features distinct querying modes based on the type of target. See AssociationProxy now provides standard column operators for a column-oriented target.

Cascading Scalar Deletes

New in version 1.3.

Given a mapping as:

class A(Base):
    __tablename__ = 'test_a'
    id = Column(Integer, primary_key=True)
    ab = relationship(
        'AB', backref='a', uselist=False)
    b = association_proxy(
        'ab', 'b', creator=lambda b: AB(b=b),
        cascade_scalar_deletes=True)


class B(Base):
    __tablename__ = 'test_b'
    id = Column(Integer, primary_key=True)
    ab = relationship('AB', backref='b', cascade='all, delete-orphan')


class AB(Base):
    __tablename__ = 'test_ab'
    a_id = Column(Integer, ForeignKey(A.id), primary_key=True)
    b_id = Column(Integer, ForeignKey(B.id), primary_key=True)

An assignment to A.b will generate an AB object:

a.b = B()

The A.b association is scalar, and includes use of the flag :paramref:`.AssociationProxy.cascade_scalar_deletes`. When set, setting A.b to None will remove A.ab as well:

a.b = None
assert a.ab is None

When :paramref:`.AssociationProxy.cascade_scalar_deletes` is not set, the association object a.ab above would remain in place.

Note that this is not the behavior for collection-based association proxies; in that case, the intermediary association object is always removed when members of the proxied collection are removed. Whether or not the row is deleted depends on the relationship cascade setting.

See also

Cascades