SQLAlchemy object-relational configuration involves the combination of Table, mapper(), and class objects to define a mapped class. declarative allows all three to be expressed at once within the class declaration. As much as possible, regular SQLAlchemy schema and ORM constructs are used directly, so that configuration between “classical” ORM usage and declarative remain highly similar.
As a simple example:
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 returns a new base class from which all mapped classes should inherit. When the class definition is completed, a new Table and mapper() will have been generated.
The resulting table and mapper are accessible via __table__ and __mapper__ attributes on the SomeClass class:
# access the mapped Table
SomeClass.__table__
# access the Mapper
SomeClass.__mapper__In the previous example, the Column objects are automatically named with the name of the attribute to which they are assigned.
To name columns explicitly with a name distinct from their mapped attribute, just give the column a name. Below, column “some_table_id” is mapped to the “id” attribute of SomeClass, but in SQL will be represented as “some_table_id”:
class SomeClass(Base):
__tablename__ = 'some_table'
id = Column("some_table_id", Integer, primary_key=True)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 = relationship(RelatedInfo)Classes which are constructed using declarative can interact freely with classes that are mapped explicitly with mapper().
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 object is intended to be accessed directly for MetaData-specific operations. Such as, to issue CREATE statements for all tables:
engine = create_engine('sqlite://')
Base.metadata.create_all(engine)The usual techniques of associating MetaData: with Engine apply, such as assigning to the bind attribute:
Base.metadata.bind = create_engine('sqlite://')To associate the engine with the declarative_base() at time of construction, the bind argument is accepted:
Base = declarative_base(bind=create_engine('sqlite://'))declarative_base() can also receive a pre-existing 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)Relationships to other classes are done in the usual way, with the added feature that the class specified to relationship() 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 = relationship("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 = relationship(User, primaryjoin=user_id == User.id)In addition to the main argument for relationship(), 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 = relationship("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 = relationship(Address,
primaryjoin=Address.user_id==User.id)Many-to-many relationships are also declared in the same way with declarative as with traditional mappings. The secondary argument to relationship() is as usual passed a Table object, which is typically declared in the traditional way. The Table usually shares the 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 = relationship("Keyword", secondary=keywords)As with traditional mapping, its generally not a good idea to use a Table as the “secondary” argument which is also mapped to a class, unless the relationship is declared with viewonly=True. Otherwise, the unit-of-work system may attempt duplicate INSERT and DELETE statements against the underlying table.
Synonyms are introduced in Using Descriptors. To define a getter/setter which proxies to an underlying attribute, use synonym() with the descriptor argument. Here we present using Python 2.6 style properties:
class MyClass(Base):
__tablename__ = 'sometable'
id = Column(Integer, primary_key=True)
_attr = Column('attr', String)
@property
def attr(self):
return self._attr
@attr.setter
def attr(self, attr):
self._attr = attr
attr = synonym('_attr', descriptor=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'
id = Column(Integer, primary_key=True)
_attr = Column('attr', String)
@synonym_for('_attr')
@property
def attr(self):
return self._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()The usage of column_property() with Declarative to define load-time, mapped SQL expressions is pretty much the same as that described in SQL Expressions as Mapped Attributes. Local columns within the same class declaration can be referenced directly:
class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
firstname = Column(String)
lastname = Column(String)
fullname = column_property(
firstname + " " + lastname
)Correlated subqueries reference the Column objects they need either from the local class definition or from remote classes:
from sqlalchemy.sql import func
class Address(Base):
__tablename__ = 'address'
id = Column('id', Integer, primary_key=True)
user_id = Column(Integer, ForeignKey('user.id'))
class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
name = Column(String)
address_count = column_property(
select([func.count(Address.id)]).\
where(Address.user_id==id)
)In the case that the address_count attribute above doesn’t have access to Address when User is defined, the address_count attribute should be added to User when both User and Address are available (i.e. there is no string based “late compilation” feature like there is with relationship() at this time). Note we reference the id column attribute of User with its class when we are no longer in the declaration of the User class:
User.address_count = column_property(
select([func.count(Address.id)]).\
where(Address.user_id==User.id)
)Table arguments other than the name, metadata, and mapped Column arguments are specified using the __table_args__ class attribute. This attribute accommodates both positional as well as keyword arguments that are normally sent to the Table constructor. The attribute can be specified in one of two forms. One is as a dictionary:
class MyClass(Base):
__tablename__ = 'sometable'
__table_args__ = {'mysql_engine':'InnoDB'}The other, a tuple of the form (arg1, arg2, ..., {kwarg1:value, ...}), which allows positional arguments to be specified as well (usually constraints):
class MyClass(Base):
__tablename__ = 'sometable'
__table_args__ = (
ForeignKeyConstraint(['id'], ['remote_table.id']),
UniqueConstraint('foo'),
{'autoload':True}
)Note that the keyword parameters dictionary is required in the tuple form even if empty.
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))
)__table__ provides a more focused point of control for establishing table metadata, while still getting most of the benefits of using declarative. An application that uses reflection might want to load table metadata elsewhere and simply pass it to declarative classes:
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
Base.metadata.reflect(some_engine)
class User(Base):
__table__ = metadata.tables['user']
class Address(Base):
__table__ = metadata.tables['address']Some configuration schemes may find it more appropriate to use __table__, such as those which already take advantage of the data-driven nature of Table to customize and/or automate schema definition. See the wiki example NamingConventions for one such example.
Declarative makes use of the mapper() function internally when it creates the mapping to the declared table. The options for mapper() are passed directly through via the __mapper_args__ class attribute. As always, arguments which reference locally mapped columns can reference them directly from within the class declaration:
from datetime import datetime
class Widget(Base):
__tablename__ = 'widgets'
id = Column(Integer, primary_key=True)
timestamp = Column(DateTime, nullable=False)
__mapper_args__ = {
'version_id_col': timestamp,
'version_id_generator': lambda v:datetime.now()
}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}A common need when using declarative is to share some functionality, often a set of columns, across many classes. The normal Python idiom would be to put this common code into a base class and have all the other classes subclass this class.
When using declarative, this need is met by using a “mixin class”. A mixin class is one that isn’t mapped to a table and doesn’t subclass the declarative Base. For example:
class MyMixin(object):
__table_args__ = {'mysql_engine': 'InnoDB'}
__mapper_args__= {'always_refresh': True}
id = Column(Integer, primary_key=True)
class MyModel(Base,MyMixin):
__tablename__ = 'test'
name = Column(String(1000))Where above, the class MyModel will contain an “id” column as well as __table_args__ and __mapper_args__ defined by the MyMixin mixin class.
The most basic way to specify a column on a mixin is by simple declaration:
class TimestampMixin(object):
created_at = Column(DateTime, default=func.now())
class MyModel(Base, TimestampMixin):
__tablename__ = 'test'
id = Column(Integer, primary_key=True)
name = Column(String(1000))Where above, all declarative classes that include TimestampMixin will also have a column created_at that applies a timestamp to all row insertions.
Those familiar with the SQLAlchemy expression language know that the object identity of clause elements defines their role in a schema. Two Table objects a and b may both have a column called id, but the way these are differentiated is that a.c.id and b.c.id are two distinct Python objects, referencing their parent tables a and b respectively.
In the case of the mixin column, it seems that only one Column object is explicitly created, yet the ultimate created_at column above must exist as a distinct Python object for each separate destination class. To accomplish this, the declarative extension creates a copy of each Column object encountered on a class that is detected as a mixin.
This copy mechanism is limited to simple columns that have no foreign keys, as a ForeignKey itself contains references to columns which can’t be properly recreated at this level. For columns that have foreign keys, as well as for the variety of mapper-level constructs that require destination-explicit context, the declared_attr() decorator (renamed from sqlalchemy.util.classproperty in 0.6.5) is provided so that patterns common to many classes can be defined as callables:
from sqlalchemy.ext.declarative import declared_attr
class ReferenceAddressMixin(object):
@declared_attr
def address_id(cls):
return Column(Integer, ForeignKey('address.id'))
class User(Base, ReferenceAddressMixin):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)Where above, the address_id class-level callable is executed at the point at which the User class is constructed, and the declarative extension can use the resulting Column object as returned by the method without the need to copy it.
Columns generated by declared_attr() can also be referenced by __mapper_args__ to a limited degree, currently by polymorphic_on and version_id_col, by specifying the classdecorator itself into the dictionary - the declarative extension will resolve them at class construction time:
class MyMixin:
@declared_attr
def type_(cls):
return Column(String(50))
__mapper_args__= {'polymorphic_on':type_}
class MyModel(Base,MyMixin):
__tablename__='test'
id = Column(Integer, primary_key=True)Relationships created by relationship() are provided with declarative mixin classes exclusively using the declared_attr() approach, eliminating any ambiguity which could arise when copying a relationship and its possibly column-bound contents. Below is an example which combines a foreign key column and a relationship so that two classes Foo and Bar can both be configured to reference a common target class via many-to-one:
class RefTargetMixin(object):
@declared_attr
def target_id(cls):
return Column('target_id', ForeignKey('target.id'))
@declared_attr
def target(cls):
return relationship("Target")
class Foo(Base, RefTargetMixin):
__tablename__ = 'foo'
id = Column(Integer, primary_key=True)
class Bar(Base, RefTargetMixin):
__tablename__ = 'bar'
id = Column(Integer, primary_key=True)
class Target(Base):
__tablename__ = 'target'
id = Column(Integer, primary_key=True)relationship() definitions which require explicit primaryjoin, order_by etc. expressions should use the string forms for these arguments, so that they are evaluated as late as possible. To reference the mixin class in these expressions, use the given cls to get it’s name:
class RefTargetMixin(object):
@declared_attr
def target_id(cls):
return Column('target_id', ForeignKey('target.id'))
@declared_attr
def target(cls):
return relationship("Target",
primaryjoin="Target.id==%s.target_id" % cls.__name__
)Like relationship(), all MapperProperty subclasses such as deferred(), column_property(), etc. ultimately involve references to columns, and therefore, when used with declarative mixins, have the declared_attr() requirement so that no reliance on copying is needed:
class SomethingMixin(object):
@declared_attr
def dprop(cls):
return deferred(Column(Integer))
class Something(Base, SomethingMixin):
__tablename__ = "something"The __tablename__ attribute in conjunction with the hierarchy of classes involved in a declarative mixin scenario controls what type of table inheritance, if any, is configured by the declarative extension.
If the __tablename__ is computed by a mixin, you may need to control which classes get the computed attribute in order to get the type of table inheritance you require.
For example, if you had a mixin that computes __tablename__ but where you wanted to use that mixin in a single table inheritance hierarchy, you can explicitly specify __tablename__ as None to indicate that the class should not have a table mapped:
from sqlalchemy.ext.declarative import declared_attr
class Tablename:
@declared_attr
def __tablename__(cls):
return cls.__name__.lower()
class Person(Base,Tablename):
id = Column(Integer, primary_key=True)
discriminator = Column('type', String(50))
__mapper_args__ = {'polymorphic_on': discriminator}
class Engineer(Person):
__tablename__ = None
__mapper_args__ = {'polymorphic_identity': 'engineer'}
primary_language = Column(String(50))Alternatively, you can make the mixin intelligent enough to only return a __tablename__ in the event that no table is already mapped in the inheritance hierarchy. To help with this, a has_inherited_table() helper function is provided that returns True if a parent class already has a mapped table.
As an example, here’s a mixin that will only allow single table inheritance:
from sqlalchemy.ext.declarative import declared_attr
from sqlalchemy.ext.declarative import has_inherited_table
class Tablename:
@declared_attr
def __tablename__(cls):
if has_inherited_table(cls):
return None
return cls.__name__.lower()
class Person(Base,Tablename):
id = Column(Integer, primary_key=True)
discriminator = Column('type', String(50))
__mapper_args__ = {'polymorphic_on': discriminator}
class Engineer(Person):
primary_language = Column(String(50))
__mapper_args__ = {'polymorphic_identity': 'engineer'}If you want to use a similar pattern with a mix of single and joined table inheritance, you would need a slightly different mixin and use it on any joined table child classes in addition to their parent classes:
from sqlalchemy.ext.declarative import declared_attr
from sqlalchemy.ext.declarative import has_inherited_table
class Tablename:
@declared_attr
def __tablename__(cls):
if (has_inherited_table(cls) and
Tablename not in cls.__bases__):
return None
return cls.__name__.lower()
class Person(Base,Tablename):
id = Column(Integer, primary_key=True)
discriminator = Column('type', String(50))
__mapper_args__ = {'polymorphic_on': discriminator}
# This is single table inheritance
class Engineer(Person):
primary_language = Column(String(50))
__mapper_args__ = {'polymorphic_identity': 'engineer'}
# This is joined table inheritance
class Manager(Person,Tablename):
id = Column(Integer, ForeignKey('person.id'), primary_key=True)
preferred_recreation = Column(String(50))
__mapper_args__ = {'polymorphic_identity': 'engineer'}In the case of __table_args__ or __mapper_args__ specified with declarative mixins, you may want to combine some parameters from several mixins with those you wish to define on the class iteself. The declared_attr() decorator can be used here to create user-defined collation routines that pull from multiple collections:
from sqlalchemy.ext.declarative import declared_attr
class MySQLSettings:
__table_args__ = {'mysql_engine':'InnoDB'}
class MyOtherMixin:
__table_args__ = {'info':'foo'}
class MyModel(Base,MySQLSettings,MyOtherMixin):
__tablename__='my_model'
@declared_attr
def __table_args__(self):
args = dict()
args.update(MySQLSettings.__table_args__)
args.update(MyOtherMixin.__table_args__)
return args
id = Column(Integer, primary_key=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 does nothing special with sessions, and is only intended as an easier way to configure mappers and Table objects. A typical application setup using scoped_session() might look like:
engine = create_engine('postgresql://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 produces appropriate Table objects and makes the appropriate mapper() calls based on the information provided declaratively in the class and any subclasses of the class.
| Parameters: |
|
|---|
Mark a class-level method as representing the definition of a mapped property or special declarative member name.
Note
@declared_attr is available as sqlalchemy.util.classproperty for SQLAlchemy versions 0.6.2, 0.6.3, 0.6.4.
@declared_attr turns the attribute into a scalar-like property that can be invoked from the uninstantiated class. Declarative treats attributes specifically marked with @declared_attr as returning a construct that is specific to mapping or declarative table configuration. The name of the attribute is that of what the non-dynamic version of the attribute would be.
@declared_attr is more often than not applicable to mixins, to define relationships that are to be applied to different implementors of the class:
class ProvidesUser(object):
"A mixin that adds a 'user' relationship to classes."
@declared_attr
def user(self):
return relationship("User")It also can be applied to mapped classes, such as to provide a “polymorphic” scheme for inheritance:
class Employee(Base):
id = Column(Integer, primary_key=True)
type = Column(String(50), nullable=False)
@declared_attr
def __tablename__(cls):
return cls.__name__.lower()
@declared_attr
def __mapper_args__(cls):
if cls.__name__ == 'Employee':
return {
"polymorphic_on":cls.type,
"polymorphic_identity":"Employee"
}
else:
return {"polymorphic_identity":cls.__name__}A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and values in kwargs.
Only keys that are present as attributes of the instance’s class are allowed. These could be, for example, any mapped columns or relationships.
Given a class, return True if any of the classes it inherits from has a mapped table, otherwise return 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))Decorator, allow a Python @property to be used in query criteria.
This is a decorator front end to comparable_property() that 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)Given a class, configure the class declaratively, using the given registry, which can be any dictionary, and MetaData object.