SQLAlchemy 0.4 Documentation

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Version: 0.4.5 Last Updated: 04/03/08 21:06:31

module sqlalchemy.ext.declarative

A simple declarative layer for SQLAlchemy ORM.

SQLAlchemy object-relational configuration involves the usage of Table, mapper(), and class objects to define the three areas of configuration. declarative moves these three types of configuration underneath the individual mapped class. Regular SQLAlchemy schema and ORM constructs are used in most cases:

from sqlalchemy.ext.declarative import declarative_base

engine = create_engine('sqlite://')
Base = declarative_base(engine)

class SomeClass(Base):
    __tablename__ = 'some_table'
    id = Column('id', Integer, primary_key=True)
    name =  Column('name', String(50))

Above, the declarative_base callable produces a new base class from which all mapped classes inherit from. When the class definition is completed, a new Table and mapper() have been generated, accessible via the __table__ and __mapper__ attributes on the SomeClass class.

You may omit the names from the Column definitions. Declarative will fill them in for you.

class SomeClass(Base):
__tablename__ = 'some_table' id = Column(Integer, primary_key=True) name = Column(String(50))

Attributes may be added to the class after its construction, and they will be added to the underlying Table and mapper() definitions as appropriate:

SomeClass.data = Column('data', Unicode)
SomeClass.related = relation(RelatedInfo)

Classes which are mapped explicitly using mapper() can interact freely with declarative classes. The declarative_base base class contains a MetaData object as well as a dictionary of all classes created against the base. So to access the above metadata and create tables we can say:

Base.metadata.create_all()

The declarative_base can also receive a pre-created MetaData object:

mymetadata = MetaData()
Base = declarative_base(metadata=mymetadata)

Relations to other classes are done in the usual way, with the added feature that the class specified to relation() may be a string name. The "class registry" associated with Base is used at mapper compilation time to resolve the name into the actual class object, which is expected to have been defined once the mapper configuration is used:

class User(Base):
    __tablename__ = 'users'

    id = Column(Integer, primary_key=True)
    name = Column(String(50))
    addresses = relation("Address", backref="user")

class Address(Base):
    __tablename__ = 'addresses'

    id = Column(Integer, primary_key=True)
    email = Column(String(50))
    user_id = Column(Integer, ForeignKey('users.id'))

Column constructs, since they are just that, are immediately usable, as below where we define a primary join condition on the Address class using them:

class Address(Base)
    __tablename__ = 'addresses'

    id = Column(Integer, primary_key=True)
    email = Column(String(50))
    user_id = Column(Integer, ForeignKey('users.id'))
    user = relation(User, primaryjoin=user_id==User.id)

Synonyms are one area where declarative needs to slightly change the usual SQLAlchemy configurational syntax. To define a getter/setter which proxies to an underlying attribute, use synonym with the descriptor argument:

class MyClass(Base):
    __tablename__ = 'sometable'

    _attr = Column('attr', String)

    def _get_attr(self):
        return self._some_attr
    def _set_attr(self, attr)
        self._some_attr = attr
    attr = synonym('_attr', descriptor=property(_get_attr, _set_attr))

The above synonym is then usable as an instance attribute as well as a class-level expression construct:

x = MyClass()
x.attr = "some value"
session.query(MyClass).filter(MyClass.attr == 'some other value').all()

As an alternative to __tablename__, a direct Table construct may be used:

class MyClass(Base):
    __table__ = Table('my_table', Base.metadata,
        Column(Integer, primary_key=True),
        Column(String(50))
    )

This is the preferred approach when using reflected tables, as below:

class MyClass(Base):
    __table__ = Table('my_table', Base.metadata, autoload=True)

Mapper arguments are specified using the __mapper_args__ class variable. Note that the column objects declared on the class are immediately usable, as in this joined-table inheritance example:

class Person(Base):
    __tablename__ = 'people'
    id = Column(Integer, primary_key=True)
    discriminator = Column(String(50))
    __mapper_args__ = {'polymorphic_on':discriminator}

class Engineer(Person):
    __tablename__ = 'engineers'
    __mapper_args__ = {'polymorphic_identity':'engineer'}
    id = Column(Integer, ForeignKey('people.id'), primary_key=True)
    primary_language = Column(String(50))

For single-table inheritance, the __tablename__ and __table__ class variables are optional on a class when the class inherits from another mapped class.

As a convenience feature, the declarative_base() sets a default constructor on classes which takes keyword arguments, and assigns them to the named attributes:

e = Engineer(primary_language='python')

Note that declarative has no integration built in with sessions, and is only intended as an optional syntax for the regular usage of mappers and Table objects. A typical application setup using scoped_session might look like:

engine = create_engine('postgres://scott:tiger@localhost/test')
Session = scoped_session(sessionmaker(transactional=True, autoflush=False, bind=engine))
Base = declarative_base()

Mapped instances then make usage of Session in the usual way.

Module Functions

def comparable_using(comparator_factory)

Decorator, allow a Python @property to be used in query criteria.

A decorator front end to comparable_property(), passes throgh the comparator_factory and the function being decorated:

@comparable_using(MyComparatorType)
@property
def prop(self):
    return 'special sauce'

The regular comparable_property() is also usable directly in a declarative setting and may be convenient for read/write properties:

prop = comparable_property(MyComparatorType)
def declarative_base(engine=None, metadata=None, mapper=None)
def declared_synonym(*args, **kwargs)

Deprecated. Use synonym(name, descriptor=prop).

def synonym_for(name, map_column=False)

Decorator, make a Python @property a query synonym for a column.

A decorator version of synonym(). The function being decorated is the 'descriptor', otherwise passes its arguments through to synonym():

@synonym_for('col')
@property
def prop(self):
    return 'special sauce'

The regular synonym() is also usable directly in a declarative setting and may be convenient for read/write properties:

prop = synonym('col', descriptor=property(_read_prop, _write_prop))
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