# SQLAlchemy 0.6.0 Documentation

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Version: 0.6.0 Last Updated: 04/18/2010 19:16:18

# associationproxy

`associationproxy` is used to create a simplified, read/write view of a relationship. It can be used to cherry-pick fields from a collection of related objects or to greatly simplify access to associated objects in an association relationship.

## Simplifying Relationships

Consider this “association object” mapping:

    users_table = Table('users', metadata,
        Column('id', Integer, primary_key=True),
        Column('name', String(64)),
    )

    keywords_table = Table('keywords', metadata,
        Column('id', Integer, primary_key=True),
        Column('keyword', String(64))
    )

    userkeywords_table = Table('userkeywords', metadata,
        Column('user_id', Integer, ForeignKey("users.id"),
               primary_key=True),
        Column('keyword_id', Integer, ForeignKey("keywords.id"),
               primary_key=True)
    )

    class User(object):
        def __init__(self, name):
            self.name = name

    class Keyword(object):
        def __init__(self, keyword):
            self.keyword = keyword

    mapper(User, users_table, properties={
        'kw': relationship(Keyword, secondary=userkeywords_table)
        })
    mapper(Keyword, keywords_table)

Above are three simple tables, modeling users, keywords and a many-to-many relationship between the two. These `Keyword` objects are little more than a container for a name, and accessing them via the relationship is awkward:

    user = User('jek')
    user.kw.append(Keyword('cheese inspector'))
    print user.kw
    # [<__main__.Keyword object at 0xb791ea0c>]
    print user.kw[0].keyword
    # 'cheese inspector'
    print [keyword.keyword for keyword in user.kw]
    # ['cheese inspector']

With `association_proxy` you have a “view” of the relationship that contains just the `.keyword` of the related objects. The proxy is a Python property, and unlike the mapper relationship, is defined in your class:

    from sqlalchemy.ext.associationproxy import association_proxy

    class User(object):
        def __init__(self, name):
            self.name = name

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

    # ...
    >>> user.kw
    [<__main__.Keyword object at 0xb791ea0c>]
    >>> user.keywords
    ['cheese inspector']
    >>> user.keywords.append('snack ninja')
    >>> user.keywords
    ['cheese inspector', 'snack ninja']
    >>> user.kw
    [<__main__.Keyword object at 0x9272a4c>, <__main__.Keyword object at 0xb7b396ec>]

The proxy is read/write. New associated objects are created on demand when values are added to the proxy, and modifying or removing an entry through the proxy also affects the underlying collection.

> - The association proxy property is backed by a mapper-defined relationship, either a collection or scalar.
> - You can access and modify both the proxy and the backing relationship. Changes in one are immediate in the other.
> - The proxy acts like the type of the underlying collection. A list gets a list-like proxy, a dict a dict-like proxy, and so on.
> - Multiple proxies for the same relationship are fine.
> - Proxies are lazy, and won’t trigger a load of the backing relationship until they are accessed.
> - The relationship is inspected to determine the type of the related objects.
> - To construct new instances, the type is called with the value being assigned, or key and value for dicts.
> - A ```creator``` function can be used to create instances instead.

Above, the `Keyword.__init__` takes a single argument `keyword`, which maps conveniently to the value being set through the proxy. A `creator` function could have been used instead if more flexibility was required.

Because the proxies are backed by a regular relationship collection, all of the usual hooks and patterns for using collections are still in effect. The most convenient behavior is the automatic setting of “parent”-type relationships on assignment. In the example above, nothing special had to be done to associate the Keyword to the User. Simply adding it to the collection is sufficient.

## Simplifying Association Object Relationships

Association proxies are also useful for keeping `association objects` out the way during regular use. For example, the `userkeywords` table might have a bunch of auditing columns that need to get updated when changes are made- columns that are updated but seldom, if ever, accessed in your application. A proxy can provide a very natural access pattern for the relationship.

    from sqlalchemy.ext.associationproxy import association_proxy

    # users_table and keywords_table tables as above, then:

    def get_current_uid():
        """Return the uid of the current user."""
        return 1  # hardcoded for this example

    userkeywords_table = Table('userkeywords', metadata,
        Column('user_id', Integer, ForeignKey("users.id"), primary_key=True),
        Column('keyword_id', Integer, ForeignKey("keywords.id"), primary_key=True),
        # add some auditing columns
        Column('updated_at', DateTime, default=datetime.now),
        Column('updated_by', Integer, default=get_current_uid, onupdate=get_current_uid),
    )

    def _create_uk_by_keyword(keyword):
        """A creator function."""
        return UserKeyword(keyword=keyword)

    class User(object):
        def __init__(self, name):
            self.name = name
        keywords = association_proxy('user_keywords', 'keyword', creator=_create_uk_by_keyword)

    class Keyword(object):
        def __init__(self, keyword):
            self.keyword = keyword
        def __repr__(self):
            return 'Keyword(%s)' % repr(self.keyword)

    class UserKeyword(object):
        def __init__(self, user=None, keyword=None):
            self.user = user
            self.keyword = keyword

    mapper(User, users_table)
    mapper(Keyword, keywords_table)
    mapper(UserKeyword, userkeywords_table, properties={
        'user': relationship(User, backref='user_keywords'),
        'keyword': relationship(Keyword),
    })

    user = User('log')
    kw1  = Keyword('new_from_blammo')

    # Creating a UserKeyword association object will add a Keyword.
    # the "user" reference assignment in the UserKeyword() constructor
    # populates "user_keywords" via backref.
    UserKeyword(user, kw1)

    # Accessing Keywords requires traversing UserKeywords
    print user.user_keywords[0]
    # <__main__.UserKeyword object at 0xb79bbbec>

    print user.user_keywords[0].keyword
    # Keyword('new_from_blammo')

    # Lots of work.

    # It's much easier to go through the association proxy!
    for kw in (Keyword('its_big'), Keyword('its_heavy'), Keyword('its_wood')):
        user.keywords.append(kw)

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

## Building Complex Views

    stocks_table = Table("stocks", meta,
       Column('symbol', String(10), primary_key=True),
       Column('last_price', Numeric)
    )

    brokers_table = Table("brokers", meta,
       Column('id', Integer,primary_key=True),
       Column('name', String(100), nullable=False)
    )

    holdings_table = Table("holdings", meta,
      Column('broker_id', Integer, ForeignKey('brokers.id'), primary_key=True),
      Column('symbol', String(10), ForeignKey('stocks.symbol'), primary_key=True),
      Column('shares', Integer)
    )

Above are three tables, modeling stocks, their brokers and the number of shares of a stock held by each broker. This situation is quite different from the association example above. `shares` is a property of the relationship, an important one that we need to use all the time.

For this example, it would be very convenient if `Broker` objects had a dictionary collection that mapped `Stock` instances to the shares held for each. That’s easy:

    from sqlalchemy.ext.associationproxy import association_proxy
    from sqlalchemy.orm.collections import attribute_mapped_collection

    def _create_holding(stock, shares):
        """A creator function, constructs Holdings from Stock and share quantity."""
        return Holding(stock=stock, shares=shares)

    class Broker(object):
        def __init__(self, name):
            self.name = name

        holdings = association_proxy('by_stock', 'shares', creator=_create_holding)

    class Stock(object):
        def __init__(self, symbol):
            self.symbol = symbol
            self.last_price = 0

    class Holding(object):
        def __init__(self, broker=None, stock=None, shares=0):
            self.broker = broker
            self.stock = stock
            self.shares = shares

    mapper(Stock, stocks_table)
    mapper(Broker, brokers_table, properties={
        'by_stock': relationship(Holding,
            collection_class=attribute_mapped_collection('stock'))
    })
    mapper(Holding, holdings_table, properties={
        'stock': relationship(Stock),
        'broker': relationship(Broker)
    })

Above, we’ve set up the `by_stock` relationship collection to act as a dictionary, using the `.stock` property of each Holding as a key.

Populating and accessing that dictionary manually is slightly inconvenient because of the complexity of the Holdings association object:

    stock = Stock('ZZK')
    broker = Broker('paj')

    broker.by_stock[stock] = Holding(broker, stock, 10)
    print broker.by_stock[stock].shares
    # 10

The `holdings` proxy we’ve added to the `Broker` class hides the details of the `Holding` while also giving access to `.shares`:

    for stock in (Stock('JEK'), Stock('STPZ')):
        broker.holdings[stock] = 123

    for stock, shares in broker.holdings.items():
        print stock, shares

    session.add(broker)
    session.commit()

    # lets take a peek at that holdings_table after committing changes to the db
    print list(holdings_table.select().execute())
    # [(1, 'ZZK', 10), (1, 'JEK', 123), (1, 'STEPZ', 123)]

Further examples can be found in the `examples/` directory in the SQLAlchemy distribution.

## API

`sqlalchemy.ext.associationproxy.``association_proxy`(target_collection, attr, **kw)

  Return a Python property implementing a view of attr over a collection.

  Implements a read/write view over an instance’s target_collection, extracting attr from each member of the collection. The property acts somewhat like this list comprehension:

      [getattr(member, *attr*)
       for member in getattr(instance, *target_collection*)]

  Unlike the list comprehension, the collection returned by the property is always in sync with target_collection, and mutations made to either collection will be reflected in both.

  Implements a Python property representing a relationship as a collection of simpler values. The proxied property will mimic the collection type of the target (list, dict or set), or, in the case of a one to one relationship, a simple scalar value.

  Parameters:

  - target_collection – Name of the relationship attribute we’ll proxy to, usually created with `relationship()`.
  - attr –

    Attribute on the associated instances we’ll proxy for.

    For example, given a target collection of [obj1, obj2], a list created by this proxy property would look like [getattr(obj1, attr), getattr(obj2, attr)]

    If the relationship is one-to-one or otherwise uselist=False, then simply: getattr(obj, attr)

  - creator –

    optional.

    When new items are added to this proxied collection, new instances of the class collected by the target collection will be created. For list and set collections, the target class constructor will be called with the ‘value’ for the new instance. For dict types, two arguments are passed: key and value.

    If you want to construct instances differently, supply a creator function that takes arguments as above and returns instances.

    For scalar relationships, creator() will be called if the target is None. If the target is present, set operations are proxied to setattr() on the associated object.

    If you have an associated object with multiple attributes, you may set up multiple association proxies mapping to different attributes. See the unit tests for examples, and for examples of how creator() functions can be used to construct the scalar relationship on-demand in this situation.

  - **kw – Passes along any other keyword arguments to `AssociationProxy`.

class `sqlalchemy.ext.associationproxy.``AssociationProxy`(target_collection, attr, creator=None, getset_factory=None, proxy_factory=None, proxy_bulk_set=None)

  A descriptor that presents a read/write view of an object attribute.

  `__init__`(target_collection, attr, creator=None, getset_factory=None, proxy_factory=None, proxy_bulk_set=None)

    Arguments are:

    target_collection
      Name of the collection we’ll proxy to, usually created with ‘relationship()’ in a mapper setup.
    attr
      Attribute on the collected instances we’ll proxy for. For example, given a target collection of [obj1, obj2], a list created by this proxy property would look like [getattr(obj1, attr), getattr(obj2, attr)]
    creator

      Optional. When new items are added to this proxied collection, new instances of the class collected by the target collection will be created. For list and set collections, the target class constructor will be called with the ‘value’ for the new instance. For dict types, two arguments are passed: key and value.

      If you want to construct instances differently, supply a ‘creator’ function that takes arguments as above and returns instances.

    getset_factory

      Optional. Proxied attribute access is automatically handled by routines that get and set values based on the attr argument for this proxy.

      If you would like to customize this behavior, you may supply a getset_factory callable that produces a tuple of getter and setter functions. The factory is called with two arguments, the abstract type of the underlying collection and this proxy instance.

    proxy_factory
      Optional. The type of collection to emulate is determined by sniffing the target collection. If your collection type can’t be determined by duck typing or you’d like to use a different collection implementation, you may supply a factory function to produce those collections. Only applicable to non-scalar relationships.
    proxy_bulk_set
      Optional, use with proxy_factory. See the _set() method for details.
  `any`(criterion=None, **kwargs)
  `contains`(obj)
  `has`(criterion=None, **kwargs)
  `target_class`
    The class the proxy is attached to.