让我们说我有一个包含三个表的数据库结构：
items
- item_id
- item_handle
attributes
- attribute_id
- attribute_name
item_attributes
- item_attribute_id
- item_id
- attribute_id
- attribute_value
我希望能够在SQLAlchemy中执行此操作：
item = Item('item1')
item.foo = 'bar'
session.add(item)
session.commit()
item1 = session.query(Item).filter_by(handle='item1').one()
print item1.foo # => 'bar'
我是SQLAlchemy的新手，我在文档中发现了这一点（http://www.sqlalchemy.org/docs/05/mappers.html#mapping-a-class-against-multiple-tables）：
j = join(items, item_attributes, items.c.item_id == item_attributes.c.item_id). \
join(attributes, item_attributes.c.attribute_id == attributes.c.attribute_id)
mapper(Item, j, properties={
'item_id': [items.c.item_id, item_attributes.c.item_id],
'attribute_id': [item_attributes.c.attribute_id, attributes.c.attribute_id],
})
它只将item_id和attribute_id添加到Item中，并且无法向Item对象添加属性。
我正在尝试使用SQLAlchemy实现什么？有没有更好的方法来构建数据库以获得“动态列”的相同行为？
答案 0 :(得分：8)
这称为entity-attribute-value模式。在SQLAlchemy示例目录下有一个示例：vertical/。
如果您正在使用PostgreSQL，那么还有hstore contrib模块可以将字符串存储到字符串映射。如果您有兴趣，那么我有一些自定义类型的代码，可以使用它来通过SQLAlchemy存储扩展属性。
存储自定义属性的另一个选项是将它们序列化为文本字段。在这种情况下，您将无法按属性进行过滤。
答案 1 :(得分：5)
对vertical / vertical.py的链接已断开。该示例已重命名为dictlike-polymorphic.py和dictlike.py.
我正在粘贴dictlike.py的内容：
"""Mapping a vertical table as a dictionary.
This example illustrates accessing and modifying a "vertical" (or
"properties", or pivoted) table via a dict-like interface. These are tables
that store free-form object properties as rows instead of columns. For
example, instead of::
# A regular ("horizontal") table has columns for 'species' and 'size'
Table('animal', metadata,
Column('id', Integer, primary_key=True),
Column('species', Unicode),
Column('size', Unicode))
A vertical table models this as two tables: one table for the base or parent
entity, and another related table holding key/value pairs::
Table('animal', metadata,
Column('id', Integer, primary_key=True))
# The properties table will have one row for a 'species' value, and
# another row for the 'size' value.
Table('properties', metadata
Column('animal_id', Integer, ForeignKey('animal.id'),
primary_key=True),
Column('key', UnicodeText),
Column('value', UnicodeText))
Because the key/value pairs in a vertical scheme are not fixed in advance,
accessing them like a Python dict can be very convenient. The example below
can be used with many common vertical schemas as-is or with minor adaptations.
"""
class VerticalProperty(object):
"""A key/value pair.
This class models rows in the vertical table.
"""
def __init__(self, key, value):
self.key = key
self.value = value
def __repr__(self):
return '<%s %r=%r>' % (self.__class__.__name__, self.key, self.value)
class VerticalPropertyDictMixin(object):
"""Adds obj[key] access to a mapped class.
This is a mixin class. It can be inherited from directly, or included
with multiple inheritence.
Classes using this mixin must define two class properties::
_property_type:
The mapped type of the vertical key/value pair instances. Will be
invoked with two positional arugments: key, value
_property_mapping:
A string, the name of the Python attribute holding a dict-based
relationship of _property_type instances.
Using the VerticalProperty class above as an example,::
class MyObj(VerticalPropertyDictMixin):
_property_type = VerticalProperty
_property_mapping = 'props'
mapper(MyObj, sometable, properties={
'props': relationship(VerticalProperty,
collection_class=attribute_mapped_collection('key'))})
Dict-like access to MyObj is proxied through to the 'props' relationship::
myobj['key'] = 'value'
# ...is shorthand for:
myobj.props['key'] = VerticalProperty('key', 'value')
myobj['key'] = 'updated value']
# ...is shorthand for:
myobj.props['key'].value = 'updated value'
print myobj['key']
# ...is shorthand for:
print myobj.props['key'].value
"""
_property_type = VerticalProperty
_property_mapping = None
__map = property(lambda self: getattr(self, self._property_mapping))
def __getitem__(self, key):
return self.__map[key].value
def __setitem__(self, key, value):
property = self.__map.get(key, None)
if property is None:
self.__map[key] = self._property_type(key, value)
else:
property.value = value
def __delitem__(self, key):
del self.__map[key]
def __contains__(self, key):
return key in self.__map
# Implement other dict methods to taste. Here are some examples:
def keys(self):
return self.__map.keys()
def values(self):
return [prop.value for prop in self.__map.values()]
def items(self):
return [(key, prop.value) for key, prop in self.__map.items()]
def __iter__(self):
return iter(self.keys())
if __name__ == '__main__':
from sqlalchemy import (MetaData, Table, Column, Integer, Unicode,
ForeignKey, UnicodeText, and_, not_)
from sqlalchemy.orm import mapper, relationship, create_session
from sqlalchemy.orm.collections import attribute_mapped_collection
metadata = MetaData()
# Here we have named animals, and a collection of facts about them.
animals = Table('animal', metadata,
Column('id', Integer, primary_key=True),
Column('name', Unicode(100)))
facts = Table('facts', metadata,
Column('animal_id', Integer, ForeignKey('animal.id'),
primary_key=True),
Column('key', Unicode(64), primary_key=True),
Column('value', UnicodeText, default=None),)
class AnimalFact(VerticalProperty):
"""A fact about an animal."""
class Animal(VerticalPropertyDictMixin):
"""An animal.
Animal facts are available via the 'facts' property or by using
dict-like accessors on an Animal instance::
cat['color'] = 'calico'
# or, equivalently:
cat.facts['color'] = AnimalFact('color', 'calico')
"""
_property_type = AnimalFact
_property_mapping = 'facts'
def __init__(self, name):
self.name = name
def __repr__(self):
return '<%s %r>' % (self.__class__.__name__, self.name)
mapper(Animal, animals, properties={
'facts': relationship(
AnimalFact, backref='animal',
collection_class=attribute_mapped_collection('key')),
})
mapper(AnimalFact, facts)
metadata.bind = 'sqlite:///'
metadata.create_all()
session = create_session()
stoat = Animal(u'stoat')
stoat[u'color'] = u'reddish'
stoat[u'cuteness'] = u'somewhat'
# dict-like assignment transparently creates entries in the
# stoat.facts collection:
print stoat.facts[u'color']
session.add(stoat)
session.flush()
session.expunge_all()
critter = session.query(Animal).filter(Animal.name == u'stoat').one()
print critter[u'color']
print critter[u'cuteness']
critter[u'cuteness'] = u'very'
print 'changing cuteness:'
metadata.bind.echo = True
session.flush()
metadata.bind.echo = False
marten = Animal(u'marten')
marten[u'color'] = u'brown'
marten[u'cuteness'] = u'somewhat'
session.add(marten)
shrew = Animal(u'shrew')
shrew[u'cuteness'] = u'somewhat'
shrew[u'poisonous-part'] = u'saliva'
session.add(shrew)
loris = Animal(u'slow loris')
loris[u'cuteness'] = u'fairly'
loris[u'poisonous-part'] = u'elbows'
session.add(loris)
session.flush()
q = (session.query(Animal).
filter(Animal.facts.any(
and_(AnimalFact.key == u'color',
AnimalFact.value == u'reddish'))))
print 'reddish animals', q.all()
# Save some typing by wrapping that up in a function:
with_characteristic = lambda key, value: and_(AnimalFact.key == key,
AnimalFact.value == value)
q = (session.query(Animal).
filter(Animal.facts.any(
with_characteristic(u'color', u'brown'))))
print 'brown animals', q.all()
q = (session.query(Animal).
filter(not_(Animal.facts.any(
with_characteristic(u'poisonous-part', u'elbows')))))
print 'animals without poisonous-part == elbows', q.all()
q = (session.query(Animal).
filter(Animal.facts.any(AnimalFact.value == u'somewhat')))
print 'any animal with any .value of "somewhat"', q.all()
# Facts can be queried as well.
q = (session.query(AnimalFact).
filter(with_characteristic(u'cuteness', u'very')))
print 'just the facts', q.all()
metadata.drop_all()