SQLAlchemy 0.5 Documentation

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Version: 0.5.0beta3 Last Updated: 08/04/08 18:36:49

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

Base = declarative_base()

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 where newly defined Table objects are collected. This is accessed via the metadata class level accessor, so to create tables we can say:

engine = create_engine('sqlite://')
Base.metadata.create_all(engine)

The Engine created above may also be directly associated with the declarative base class using the engine keyword argument, where it will be associated with the underlying MetaData object and allow SQL operations involving that metadata and its tables to make use of that engine automatically:

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

Or, as MetaData allows, at any time using the bind attribute:

Base.metadata.bind = create_engine('sqlite://')

The declarative_base can also receive a pre-created MetaData object, which allows a declarative setup to be associated with an already existing traditional collection of Table objects:

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)

In addition to the main argument for relation, other arguments which depend upon the columns present on an as-yet undefined class may also be specified as strings. These strings are evaluated as Python expressions. The full namespace available within this evaluation includes all classes mapped for this declarative base, as well as the contents of the sqlalchemy package, including expression functions like desc and func:

class User(Base):
    # ....
    addresses = relation("Address", order_by="desc(Address.email)",
        primaryjoin="Address.user_id==User.id")

As an alternative to string-based attributes, attributes may also be defined after all classes have been created. Just add them to the target class after the fact:

User.addresses = relation(Address, primaryjoin=Address.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. The Column objects, which in this case require their names, will be added to the mapping just like a regular mapping to a table:

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

Other table-based attributes include __table_args__, which is either a dictionary as in:

class MyClass(Base)
    __tablename__ = 'sometable'
    __table_args__ = {'mysql_engine':'InnoDB'}

or a dictionary-containing tuple in the form (arg1, arg2, ..., {kwarg1:value, ...}), as in:

class MyClass(Base)
    __tablename__ = 'sometable'
    __table_args__ = (ForeignKeyConstraint(['id'], ['remote_table.id']), {'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(autocommit=False,
                                      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 through 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(bind=None, metadata=None, mapper=None, cls=, name='Base', constructor=, metaclass=, engine=None)

Construct a base class for declarative class definitions.

The new base class will be given a metaclass that invokes instrument_declarative() upon each subclass definition, and routes later Column- and Mapper-related attribute assignments made on the class into Table and Mapper assignments. See the declarative module documentation for examples.

bind
An optional Connectable, will be assigned to the metadata.bind. The engine keyword argument is a deprecated synonym for bind.
metadata
An optional MetaData instance. All Tables implicitly declared by subclasses of the base will share this MetaData. A MetaData instance will be create if none is provided. The MetaData instance will be available via the metadata attribute of the generated declarative base class.
mapper
An optional callable, defaults to sqlalchemy.orm.mapper. Will be used to map subclasses to their Tables.
cls
Defaults to object. A type to use as the base for the generated declarative base class. May be a type or tuple of types.
name
Defaults to 'Base', Python's internal display name for the generated class. Customizing this is not required, but can improve clarity in tracebacks and debugging.
constructor
Defaults to declarative._declarative_constructor, an __init__ implementation that assigns **kwargs for declared fields and relations to an instance. If None is supplied, no __init__ will be installed and construction will fall back to cls.__init__ with normal Python semantics.
metaclass
Defaults to DeclarativeMeta. A metaclass or __metaclass__ compatible callable to use as the meta type of the generated declarative base class.
def instrument_declarative(cls, registry, metadata)

Given a class, configure the class declaratively, using the given registry (any dictionary) and MetaData object. This operation does not assume any kind of class hierarchy.

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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