2009-08-19 5 views
9

Lassen Sie uns sagen, dass ich eine Datenbankstruktur mit drei Tabellen haben, die wie folgt aussehen:Wie man eine Klasse gegen mehrere Tabellen mit SQLAlchemy abbildet?

items 
- item_id 
- item_handle 

attributes 
- attribute_id 
- attribute_name 

item_attributes 
- item_attribute_id 
- item_id 
- attribute_id 
- attribute_value 

Ich möchte in der Lage sein, dies in SQLAlchemy zu tun:

item = Item('item1') 
item.foo = 'bar' 

session.add(item) 
session.commit() 

item1 = session.query(Item).filter_by(handle='item1').one() 
print item1.foo # => 'bar' 

Ich bin neu in SQLAlchemy und ich fand dies in der Dokumentation (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], 
}) 

Es fügt nur item_id und attribute_id auf Punkt und es ist nicht möglich hinzufügen Attribute zum Objekt Objekt.

Was versuche ich mit SQLAlchemy zu erreichen? Gibt es eine bessere Möglichkeit, die Datenbank zu strukturieren, um das gleiche Verhalten von "dynamischen Spalten" zu erhalten?

+0

Link rot http://docs.sqlalchemy.org/en/latest/orm/nonstandard_mappings.html – slashdottir

Antwort

8

Dies wird entity-attribute-value Muster genannt. Es gibt ein Beispiel dazu unter dem SQLAlchemy-Beispielverzeichnis: vertical/.

Wenn Sie PostgreSQL verwenden, dann gibt es auch das hstore contrib-Modul, das eine Zeichenkette zur Zeichenfolgenzuordnung speichern kann. Wenn Sie interessiert sind, dann habe ich Code für einen benutzerdefinierten Typ, der es ermöglicht, erweiterte Attribute über SQLAlchemy zu speichern.

Eine weitere Option zum Speichern von benutzerdefinierten Attributen besteht darin, sie in ein Textfeld zu serialisieren. In diesem Fall verlieren Sie die Fähigkeit, nach Attributen zu filtern.

+0

Kam über diese Antwort (genau das, was ich suchte), aber der Link war gebrochen. Siehe meine Antwort unten für eine der Beispieldateien. –

+2

Sie könnten den Link gerade repariert haben. –

+1

Ich musste den Repo klonen, um den Code zu bekommen. Ich kann nicht mehr über trac auf den Code zugreifen. –

5

Die Verknüpfung zu vertical/vertical.py ist unterbrochen. Das Beispiel hatte dictlike-polymorphic.py umbenannt und dictlike.py.

ich in den Inhalten dictlike.py am Einfügen:

"""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()