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.
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.
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')]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
# 10The 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.
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: |
|
|---|
A descriptor that presents a read/write view of an object attribute.
Arguments are:
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.
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.