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(Integer, primary_key=True)
name = Column(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.
Column objects may be explicitly named, including using a different name than the attribute in which they are associated. The column will be assigned to the Table using the given name, and mapped to the class using the attribute name:
class SomeClass(Base):
__tablename__ = 'some_table'
id = Column("some_table_id", Integer, primary_key=True)
name = Column("name", String(50))Otherwise, you may omit the names from the Column definitions. Declarative will set the name attribute on the column when the class is initialized:
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. It is recommended, though not required, that all tables share the same underlying MetaData object, so that string-configured ForeignKey references can be resolved without issue.
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 bind 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(bind=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)There’s nothing special about many-to-many with declarative. The secondary argument to relation() still requires a Table object, not a declarative class. The Table should share the same MetaData object used by the declarative base:
keywords = Table('keywords', Base.metadata,
Column('author_id', Integer, ForeignKey('authors.id')),
Column('keyword_id', Integer, ForeignKey('keywords.id'))
)
class Author(Base):
__tablename__ = 'authors'
id = Column(Integer, primary_key=True)
keywords = relation("Keyword", secondary=keywords)You should generally not map a class and also specify its table in a many-to-many relation, since the ORM may issue duplicate INSERT and DELETE statements.
Synonyms are introduced in Using Descriptors. 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()For simple getters, the synonym_for() decorator can be used in conjunction with @property:
class MyClass(Base):
__tablename__ = 'sometable'
_attr = Column('attr', String)
@synonym_for('_attr')
@property
def attr(self):
return self._some_attrSimilarly, comparable_using() is a front end for the comparable_property() ORM function:
class MyClass(Base):
__tablename__ = 'sometable'
name = Column('name', String)
@comparable_using(MyUpperCaseComparator)
@property
def uc_name(self):
return self.name.upper()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, which is a dictionary that accepts the same names as the mapper function accepts as keywords:
class Widget(Base):
__tablename__ = 'widgets'
id = Column(Integer, primary_key=True)
__mapper_args__ = {'extension': MyWidgetExtension()}Declarative supports all three forms of inheritance as intuitively as possible. The inherits mapper keyword argument is not needed, as declarative will determine this from the class itself. The various “polymorphic” keyword arguments are specified using __mapper_args__.
Joined table inheritance is defined as a subclass that defines its own table:
class Person(Base):
__tablename__ = 'people'
id = Column(Integer, primary_key=True)
discriminator = Column('type', 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))Note that above, the Engineer.id attribute, since it shares the same attribute name as the Person.id attribute, will in fact represent the people.id and engineers.id columns together, and will render inside a query as "people.id". To provide the Engineer class with an attribute that represents only the engineers.id column, give it a different attribute name:
class Engineer(Person):
__tablename__ = 'engineers'
__mapper_args__ = {'polymorphic_identity': 'engineer'}
engineer_id = Column('id', Integer, ForeignKey('people.id'), primary_key=True)
primary_language = Column(String(50))Single table inheritance is defined as a subclass that does not have its own table; you just leave out the __table__ and __tablename__ attributes:
class Person(Base):
__tablename__ = 'people'
id = Column(Integer, primary_key=True)
discriminator = Column('type', String(50))
__mapper_args__ = {'polymorphic_on': discriminator}
class Engineer(Person):
__mapper_args__ = {'polymorphic_identity': 'engineer'}
primary_language = Column(String(50))When the above mappers are configured, the Person class is mapped to the people table before the primary_language column is defined, and this column will not be included in its own mapping. When Engineer then defines the primary_language column, the column is added to the people table so that it is included in the mapping for Engineer and is also part of the table’s full set of columns. Columns which are not mapped to Person are also excluded from any other single or joined inheriting classes using the exclude_properties mapper argument. Below, Manager will have all the attributes of Person and Manager but not the primary_language attribute of Engineer:
class Manager(Person):
__mapper_args__ = {'polymorphic_identity': 'manager'}
golf_swing = Column(String(50))The attribute exclusion logic is provided by the exclude_properties mapper argument, and declarative’s default behavior can be disabled by passing an explicit exclude_properties collection (empty or otherwise) to the __mapper_args__.
Concrete is defined as a subclass which has its own table and sets the concrete keyword argument to True:
class Person(Base):
__tablename__ = 'people'
id = Column(Integer, primary_key=True)
name = Column(String(50))
class Engineer(Person):
__tablename__ = 'engineers'
__mapper_args__ = {'concrete':True}
id = Column(Integer, primary_key=True)
primary_language = Column(String(50))
name = Column(String(50))Usage of an abstract base class is a little less straightforward as it requires usage of polymorphic_union():
engineers = Table('engineers', Base.metadata,
Column('id', Integer, primary_key=True),
Column('name', String(50)),
Column('primary_language', String(50))
)
managers = Table('managers', Base.metadata,
Column('id', Integer, primary_key=True),
Column('name', String(50)),
Column('golf_swing', String(50))
)
punion = polymorphic_union({
'engineer':engineers,
'manager':managers
}, 'type', 'punion')
class Person(Base):
__table__ = punion
__mapper_args__ = {'polymorphic_on':punion.c.type}
class Engineer(Person):
__table__ = engineers
__mapper_args__ = {'polymorphic_identity':'engineer', 'concrete':True}
class Manager(Person):
__table__ = managers
__mapper_args__ = {'polymorphic_identity':'manager', 'concrete':True}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.
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.
| Parameters: |
|
|---|
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))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)