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)
When an explicit join condition or other configuration which depends on multiple classes cannot be defined immediately due to some classes not yet being available, these can be defined after all classes have been created. Attributes which are added to the class after its creation are associated with the Table/mapping in the same way as if they had been defined inline:
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)) )
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.
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)
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))