SQLAlchemy object-relational configuration involves the use of Table, mapper(), and class objects to define the three areas of configuration. declarative allows all three types of configuration to be expressed declaratively on an individual mapped class. Regular SQLAlchemy schema elements and ORM constructs are used in most cases.
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, accessible via the __table__ and __mapper__ attributes on the SomeClass class.
In the above example, the Column objects are automatically named with the name of the attribute to which they are assigned.
They can also be explicitly named, and that name does not have to be the same as name assigned on the class. 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))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 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://'))Alternatively, by way of the normal MetaData behaviour, the bind attribute of the class level accessor can be assigned at any time as follows:
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)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 (note that relationship() is only available as of SQLAlchemy 0.6beta2, and in all prior versions is known as relation(), including 0.5 and 0.4). 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)There’s nothing special about many-to-many with declarative. The secondary argument to relationship() 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 = relationship("Keyword", secondary=keywords)You should generally not map a class and also specify its table in a many-to-many relationship, 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()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))
)Configuration of mappers is done with the mapper() function and all the possible mapper configuration parameters can be found in the documentation for that function.
mapper() is still used by declaratively mapped classes and keyword parameters to the function can be passed by placing them in the __mapper_args__ class variable:
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}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 “mix-in class”. A mix-in 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__=dict(always_refresh=True)
id = Column(Integer, primary_key=True)
def foo(self):
return 'bar'+str(self.id)
class MyModel(Base,MyMixin):
__tablename__='test'
name = Column(String(1000), nullable=False, index=True)As the above example shows, __table_args__ and __mapper_args__ can both be abstracted out into a mix-in if you use common values for these across many classes.
However, particularly in the case of __table_args__, you may want to combine some parameters from several mix-ins with those you wish to define on the class iteself. To help with this, a classproperty() decorator is provided that lets you implement a class property with a function. For example:
from sqlalchemy.util import classproperty
class MySQLSettings:
__table_args__ = {'mysql_engine':'InnoDB'}
class MyOtherMixin:
__table_args__ = {'info':'foo'}
class MyModel(Base,MySQLSettings,MyOtherMixin):
__tablename__='my_model'
@classproperty
def __table_args__(self):
args = dict()
args.update(MySQLSettings.__table_args__)
args.update(MyOtherMixin.__table_args__)
return args
id = Column(Integer, primary_key=True)The __tablename__ attribute in conjunction with the hierarchy of the classes involved controls what type of table inheritance, if any, is configured by the declarative extension.
If the __tablename__ is computed by a mix-in, 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 mix-in that computes __tablename__ but where you wanted to use that mix-in 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.util import classproperty
class Tablename:
@classproperty
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 mix-in 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 examply, here’s a mix-in that will only allow single table inheritance:
from sqlalchemy.util import classproperty
from sqlalchemy.ext.declarative import has_inherited_table
class Tablename:
@classproperty
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):
__tablename__ = None
__mapper_args__ = {'polymorphic_identity': 'engineer'}
primary_language = Column(String(50))If you want to use a similar pattern with a mix of single and joined table inheritance, you would need a slightly different mix-in and use it on any joined table child classes in addition to their parent classes:
from sqlalchemy.util import classproperty
from sqlalchemy.ext.declarative import has_inherited_table
class Tablename:
@classproperty
def __tablename__(cls):
if (decl.has_inherited_table(cls) and
TableNameMixin 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}
class Engineer(Person):
# This is single table inheritance
__tablename__ = None
__mapper_args__ = {'polymorphic_identity': 'engineer'}
primary_language = Column(String(50))
class Manager(Person,Tablename):
# This is joinded table inheritance
__tablename__ = None
__mapper_args__ = {'polymorphic_identity': 'engineer'}
preferred_recreation = Column(String(50))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: |
|
|---|
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.
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)