Release: 0.9.7 | Release Date: July 22, 2014

SQLAlchemy 0.9 Documentation

Declarative

Synopsis

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__

Defining Attributes

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.

Accessing the MetaData

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)

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)

Configuring Relationships

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")

For the case where more than one module contains a class of the same name, string class names can also be specified as module-qualified paths within any of these string expressions:

class User(Base):
    # ....
    addresses = relationship("myapp.model.address.Address",
                         order_by="desc(myapp.model.address.Address.email)",
                         primaryjoin="myapp.model.address.Address.user_id=="
                                        "myapp.model.user.User.id")

The qualified path can be any partial path that removes ambiguity between the names. For example, to disambiguate between myapp.model.address.Address and myapp.model.lookup.Address, we can specify address.Address or lookup.Address:

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

New in version 0.8: module-qualified paths can be used when specifying string arguments with Declarative, in order to specify specific modules.

Two alternatives also exist to using string-based attributes. A lambda can also be used, which will be evaluated after all mappers have been configured:

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

Or, the relationship can be added to the class explicitly after the classes are available:

User.addresses = relationship(Address,
                          primaryjoin=Address.user_id==User.id)

Configuring Many-to-Many Relationships

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)

Like other relationship() arguments, a string is accepted as well, passing the string name of the table as defined in the Base.metadata.tables collection:

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.

Defining SQL Expressions

See SQL Expressions as Mapped Attributes for examples on declaratively mapping attributes to SQL expressions.

Table Configuration

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, where each argument is positional (usually constraints):

class MyClass(Base):
    __tablename__ = 'sometable'
    __table_args__ = (
            ForeignKeyConstraint(['id'], ['remote_table.id']),
            UniqueConstraint('foo'),
            )

Keyword arguments can be specified with the above form by specifying the last argument as a dictionary:

class MyClass(Base):
    __tablename__ = 'sometable'
    __table_args__ = (
            ForeignKeyConstraint(['id'], ['remote_table.id']),
            UniqueConstraint('foo'),
            {'autoload':True}
            )

Using a Hybrid Approach with __table__

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 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.

Note that when the __table__ approach is used, the object is immediately usable as a plain Table within the class declaration body itself, as a Python class is only another syntactical block. Below this is illustrated by using the id column in the primaryjoin condition of a relationship():

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

    widgets = relationship(Widget,
                primaryjoin=Widget.myclass_id==__table__.c.id)

Similarly, mapped attributes which refer to __table__ can be placed inline, as below where we assign the name column to the attribute _name, generating a synonym for name:

from sqlalchemy.ext.declarative import synonym_for

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

    _name = __table__.c.name

    @synonym_for("_name")
    def name(self):
        return "Name: %s" % _name

Using Reflection with Declarative

It’s easy to set up a Table that uses autoload=True in conjunction with a mapped class:

class MyClass(Base):
    __table__ = Table('mytable', Base.metadata,
                    autoload=True, autoload_with=some_engine)

However, one improvement that can be made here is to not require the Engine to be available when classes are being first declared. To achieve this, use the DeferredReflection mixin, which sets up mappings only after a special prepare(engine) step is called:

from sqlalchemy.ext.declarative import declarative_base, DeferredReflection

Base = declarative_base(cls=DeferredReflection)

class Foo(Base):
    __tablename__ = 'foo'
    bars = relationship("Bar")

class Bar(Base):
    __tablename__ = 'bar'

    # illustrate overriding of "bar.foo_id" to have
    # a foreign key constraint otherwise not
    # reflected, such as when using MySQL
    foo_id = Column(Integer, ForeignKey('foo.id'))

Base.prepare(e)

New in version 0.8: Added DeferredReflection.

Mapper Configuration

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()
                }

Inheritance Configuration

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

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, with the “Engineer.id” column taking precedence if queried directly. 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))

Changed in version 0.7: joined table inheritance favors the subclass column over that of the superclass, such as querying above for Engineer.id. Prior to 0.7 this was the reverse.

Single Table Inheritance

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__.

Resolving Column Conflicts

Note above that the primary_language and golf_swing columns are “moved up” to be applied to Person.__table__, as a result of their declaration on a subclass that has no table of its own. A tricky case comes up when two subclasses want to specify the same column, as below:

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'}
    start_date = Column(DateTime)

class Manager(Person):
    __mapper_args__ = {'polymorphic_identity': 'manager'}
    start_date = Column(DateTime)

Above, the start_date column declared on both Engineer and Manager will result in an error:

sqlalchemy.exc.ArgumentError: Column 'start_date' on class
<class '__main__.Manager'> conflicts with existing
column 'people.start_date'

In a situation like this, Declarative can’t be sure of the intent, especially if the start_date columns had, for example, different types. A situation like this can be resolved by using declared_attr to define the Column conditionally, taking care to return the existing column via the parent __table__ if it already exists:

from sqlalchemy.ext.declarative import declared_attr

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'}

    @declared_attr
    def start_date(cls):
        "Start date column, if not present already."
        return Person.__table__.c.get('start_date', Column(DateTime))

class Manager(Person):
    __mapper_args__ = {'polymorphic_identity': 'manager'}

    @declared_attr
    def start_date(cls):
        "Start date column, if not present already."
        return Person.__table__.c.get('start_date', Column(DateTime))

Above, when Manager is mapped, the start_date column is already present on the Person class. Declarative lets us return that Column as a result in this case, where it knows to skip re-assigning the same column. If the mapping is mis-configured such that the start_date column is accidentally re-assigned to a different table (such as, if we changed Manager to be joined inheritance without fixing start_date), an error is raised which indicates an existing Column is trying to be re-assigned to a different owning Table.

New in version 0.8: declared_attr can be used on a non-mixin class, and the returned Column or other mapped attribute will be applied to the mapping as any other attribute. Previously, the resulting attribute would be ignored, and also result in a warning being emitted when a subclass was created.

New in version 0.8: declared_attr, when used either with a mixin or non-mixin declarative class, can return an existing Column already assigned to the parent Table, to indicate that the re-assignment of the Column should be skipped, however should still be mapped on the target class, in order to resolve duplicate column conflicts.

The same concept can be used with mixin classes (see Mixin and Custom Base Classes):

class Person(Base):
    __tablename__ = 'people'
    id = Column(Integer, primary_key=True)
    discriminator = Column('type', String(50))
    __mapper_args__ = {'polymorphic_on': discriminator}

class HasStartDate(object):
    @declared_attr
    def start_date(cls):
        return cls.__table__.c.get('start_date', Column(DateTime))

class Engineer(HasStartDate, Person):
    __mapper_args__ = {'polymorphic_identity': 'engineer'}

class Manager(HasStartDate, Person):
    __mapper_args__ = {'polymorphic_identity': 'manager'}

The above mixin checks the local __table__ attribute for the column. Because we’re using single table inheritance, we’re sure that in this case, cls.__table__ refers to People.__table__. If we were mixing joined- and single-table inheritance, we might want our mixin to check more carefully if cls.__table__ is really the Table we’re looking for.

Concrete Table Inheritance

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(), which needs to be created with the Table objects before the class is built:

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}

Using the Concrete Helpers

Helper classes provides a simpler pattern for concrete inheritance. With these objects, the __declare_first__ helper is used to configure the “polymorphic” loader for the mapper after all subclasses have been declared.

New in version 0.7.3.

An abstract base can be declared using the AbstractConcreteBase class:

from sqlalchemy.ext.declarative import AbstractConcreteBase

class Employee(AbstractConcreteBase, Base):
    pass

To have a concrete employee table, use ConcreteBase instead:

from sqlalchemy.ext.declarative import ConcreteBase

class Employee(ConcreteBase, Base):
    __tablename__ = 'employee'
    employee_id = Column(Integer, primary_key=True)
    name = Column(String(50))
    __mapper_args__ = {
                    'polymorphic_identity':'employee',
                    'concrete':True}

Either Employee base can be used in the normal fashion:

class Manager(Employee):
    __tablename__ = 'manager'
    employee_id = Column(Integer, primary_key=True)
    name = Column(String(50))
    manager_data = Column(String(40))
    __mapper_args__ = {
                    'polymorphic_identity':'manager',
                    'concrete':True}

class Engineer(Employee):
    __tablename__ = 'engineer'
    employee_id = Column(Integer, primary_key=True)
    name = Column(String(50))
    engineer_info = Column(String(40))
    __mapper_args__ = {'polymorphic_identity':'engineer',
                    'concrete':True}

The AbstractConcreteBase class is itself mapped, and can be used as a target of relationships:

class Company(Base):
    __tablename__ = 'company'

    id = Column(Integer, primary_key=True)
    employees = relationship("Employee",
                    primaryjoin="Company.id == Employee.company_id")

Changed in version 0.9.3: Support for use of AbstractConcreteBase as the target of a relationship() has been improved.

It can also be queried directly:

for employee in session.query(Employee).filter(Employee.name == 'qbert'):
    print(employee)

Mixin and Custom Base Classes

A common need when using declarative is to share some functionality, such as a set of common columns, some common table options, or other mapped properties, across many classes. The standard Python idioms for this is to have the classes inherit from a base which includes these common features.

When using declarative, this idiom is allowed via the usage of a custom declarative base class, as well as a “mixin” class which is inherited from in addition to the primary base. Declarative includes several helper features to make this work in terms of how mappings are declared. An example of some commonly mixed-in idioms is below:

from sqlalchemy.ext.declarative import declared_attr

class MyMixin(object):

    @declared_attr
    def __tablename__(cls):
        return cls.__name__.lower()

    __table_args__ = {'mysql_engine': 'InnoDB'}
    __mapper_args__= {'always_refresh': True}

    id =  Column(Integer, primary_key=True)

class MyModel(MyMixin, Base):
    name = Column(String(1000))

Where above, the class MyModel will contain an “id” column as the primary key, a __tablename__ attribute that derives from the name of the class itself, as well as __table_args__ and __mapper_args__ defined by the MyMixin mixin class.

There’s no fixed convention over whether MyMixin precedes Base or not. Normal Python method resolution rules apply, and the above example would work just as well with:

class MyModel(Base, MyMixin):
    name = Column(String(1000))

This works because Base here doesn’t define any of the variables that MyMixin defines, i.e. __tablename__, __table_args__, id, etc. If the Base did define an attribute of the same name, the class placed first in the inherits list would determine which attribute is used on the newly defined class.

Augmenting the Base

In addition to using a pure mixin, most of the techniques in this section can also be applied to the base class itself, for patterns that should apply to all classes derived from a particular base. This is achieved using the cls argument of the declarative_base() function:

from sqlalchemy.ext.declarative import declared_attr

class Base(object):
    @declared_attr
    def __tablename__(cls):
        return cls.__name__.lower()

    __table_args__ = {'mysql_engine': 'InnoDB'}

    id =  Column(Integer, primary_key=True)

from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base(cls=Base)

class MyModel(Base):
    name = Column(String(1000))

Where above, MyModel and all other classes that derive from Base will have a table name derived from the class name, an id primary key column, as well as the “InnoDB” engine for MySQL.

Mixing in Columns

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(TimestampMixin, Base):
    __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 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(ReferenceAddressMixin, Base):
    __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.

Changed in version >: 0.6.5 Rename 0.6.5 sqlalchemy.util.classproperty into declared_attr.

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(MyMixin, Base):
    __tablename__='test'
    id =  Column(Integer, primary_key=True)

Mixing in Relationships

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(RefTargetMixin, Base):
    __tablename__ = 'foo'
    id = Column(Integer, primary_key=True)

class Bar(RefTargetMixin, Base):
    __tablename__ = 'bar'
    id = Column(Integer, primary_key=True)

class Target(Base):
    __tablename__ = 'target'
    id = Column(Integer, primary_key=True)

Using Advanced Relationship Arguments (e.g. primaryjoin, etc.)

relationship() definitions which require explicit primaryjoin, order_by etc. expressions should in all but the most simplistic cases use late bound forms for these arguments, meaning, using either the string form or a lambda. The reason for this is that the related Column objects which are to be configured using @declared_attr are not available to another @declared_attr attribute; while the methods will work and return new Column objects, those are not the Column objects that Declarative will be using as it calls the methods on its own, thus using different Column objects.

The canonical example is the primaryjoin condition that depends upon another mixed-in column:

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==cls.target_id   # this is *incorrect*
        )

Mapping a class using the above mixin, we will get an error like:

sqlalchemy.exc.InvalidRequestError: this ForeignKey's parent column is not
yet associated with a Table.

This is because the target_id Column we’ve called upon in our target() method is not the same Column that declarative is actually going to map to our table.

The condition above is resolved using a lambda:

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=lambda: Target.id==cls.target_id
        )

or alternatively, the string form (which ultimately generates a lambda):

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__
        )

Mixing in deferred(), column_property(), and other MapperProperty classes

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(SomethingMixin, Base):
    __tablename__ = "something"

Mixing in Association Proxy and Other Attributes

Mixins can specify user-defined attributes as well as other extension units such as association_proxy(). The usage of declared_attr is required in those cases where the attribute must be tailored specifically to the target subclass. An example is when constructing multiple association_proxy() attributes which each target a different type of child object. Below is an association_proxy() / mixin example which provides a scalar list of string values to an implementing class:

from sqlalchemy import Column, Integer, ForeignKey, String
from sqlalchemy.orm import relationship
from sqlalchemy.ext.associationproxy import association_proxy
from sqlalchemy.ext.declarative import declarative_base, declared_attr

Base = declarative_base()

class HasStringCollection(object):
    @declared_attr
    def _strings(cls):
        class StringAttribute(Base):
            __tablename__ = cls.string_table_name
            id = Column(Integer, primary_key=True)
            value = Column(String(50), nullable=False)
            parent_id = Column(Integer,
                            ForeignKey('%s.id' % cls.__tablename__),
                            nullable=False)
            def __init__(self, value):
                self.value = value

        return relationship(StringAttribute)

    @declared_attr
    def strings(cls):
        return association_proxy('_strings', 'value')

class TypeA(HasStringCollection, Base):
    __tablename__ = 'type_a'
    string_table_name = 'type_a_strings'
    id = Column(Integer(), primary_key=True)

class TypeB(HasStringCollection, Base):
    __tablename__ = 'type_b'
    string_table_name = 'type_b_strings'
    id = Column(Integer(), primary_key=True)

Above, the HasStringCollection mixin produces a relationship() which refers to a newly generated class called StringAttribute. The StringAttribute class is generated with its own Table definition which is local to the parent class making usage of the HasStringCollection mixin. It also produces an association_proxy() object which proxies references to the strings attribute onto the value attribute of each StringAttribute instance.

TypeA or TypeB can be instantiated given the constructor argument strings, a list of strings:

ta = TypeA(strings=['foo', 'bar'])
tb = TypeA(strings=['bat', 'bar'])

This list will generate a collection of StringAttribute objects, which are persisted into a table that’s local to either the type_a_strings or type_b_strings table:

>>> print ta._strings
[<__main__.StringAttribute object at 0x10151cd90>,
    <__main__.StringAttribute object at 0x10151ce10>]

When constructing the association_proxy(), the declared_attr decorator must be used so that a distinct association_proxy() object is created for each of the TypeA and TypeB classes.

New in version 0.8: declared_attr is usable with non-mapped attributes, including user-defined attributes as well as association_proxy().

Controlling table inheritance with mixins

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(Tablename, Base):
    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(object):
    @declared_attr
    def __tablename__(cls):
        if has_inherited_table(cls):
            return None
        return cls.__name__.lower()

class Person(Tablename, Base):
    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'}

Combining Table/Mapper Arguments from Multiple Mixins

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(object):
    __table_args__ = {'mysql_engine':'InnoDB'}

class MyOtherMixin(object):
    __table_args__ = {'info':'foo'}

class MyModel(MySQLSettings, MyOtherMixin, Base):
    __tablename__='my_model'

    @declared_attr
    def __table_args__(cls):
        args = dict()
        args.update(MySQLSettings.__table_args__)
        args.update(MyOtherMixin.__table_args__)
        return args

    id =  Column(Integer, primary_key=True)

Creating Indexes with Mixins

To define a named, potentially multicolumn Index that applies to all tables derived from a mixin, use the “inline” form of Index and establish it as part of __table_args__:

class MyMixin(object):
    a =  Column(Integer)
    b =  Column(Integer)

    @declared_attr
    def __table_args__(cls):
        return (Index('test_idx_%s' % cls.__tablename__, 'a', 'b'),)

class MyModel(MyMixin, Base):
    __tablename__ = 'atable'
    c =  Column(Integer,primary_key=True)

Special Directives

__declare_last__()

The __declare_last__() hook allows definition of a class level function that is automatically called by the MapperEvents.after_configured() event, which occurs after mappings are assumed to be completed and the ‘configure’ step has finished:

class MyClass(Base):
    @classmethod
    def __declare_last__(cls):
        ""
        # do something with mappings

New in version 0.7.3.

__declare_first__()

Like __declare_last__(), but is called at the beginning of mapper configuration via the MapperEvents.before_configured() event:

class MyClass(Base):
    @classmethod
    def __declare_first__(cls):
        ""
        # do something before mappings are configured

New in version 0.9.3.

__abstract__

__abstract__ causes declarative to skip the production of a table or mapper for the class entirely. A class can be added within a hierarchy in the same way as mixin (see Mixin and Custom Base Classes), allowing subclasses to extend just from the special class:

class SomeAbstractBase(Base):
    __abstract__ = True

    def some_helpful_method(self):
        ""

    @declared_attr
    def __mapper_args__(cls):
        return {"helpful mapper arguments":True}

class MyMappedClass(SomeAbstractBase):
    ""

One possible use of __abstract__ is to use a distinct MetaData for different bases:

Base = declarative_base()

class DefaultBase(Base):
    __abstract__ = True
    metadata = MetaData()

class OtherBase(Base):
    __abstract__ = True
    metadata = MetaData()

Above, classes which inherit from DefaultBase will use one MetaData as the registry of tables, and those which inherit from OtherBase will use a different one. The tables themselves can then be created perhaps within distinct databases:

DefaultBase.metadata.create_all(some_engine)
OtherBase.metadata_create_all(some_other_engine)

New in version 0.7.3.

Class Constructor

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')

Sessions

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.

API Reference

sqlalchemy.ext.declarative.declarative_base(bind=None, metadata=None, mapper=None, cls=<type 'object'>, name='Base', constructor=<function __init__ at 0x7f1efd509488>, class_registry=None, metaclass=<class 'sqlalchemy.ext.declarative.api.DeclarativeMeta'>)

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:
  • bind – An optional Connectable, will be assigned the bind attribute on the MetaData instance.
  • metadata – An optional MetaData instance. All Table objects implicitly declared by subclasses of the base will share this MetaData. A MetaData instance will be created 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 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 class or tuple of classes.
  • name – Defaults to Base. The display name for the generated class. Customizing this is not required, but can improve clarity in tracebacks and debugging.
  • constructor – Defaults to _declarative_constructor(), an __init__ implementation that assigns **kwargs for declared fields and relationships to an instance. If None is supplied, no __init__ will be provided and construction will fall back to cls.__init__ by way of the normal Python semantics.
  • class_registry – optional dictionary that will serve as the registry of class names-> mapped classes when string names are used to identify classes inside of relationship() and others. Allows two or more declarative base classes to share the same registry of class names for simplified inter-base relationships.
  • metaclass – Defaults to DeclarativeMeta. A metaclass or __metaclass__ compatible callable to use as the meta type of the generated declarative base class.

See also

as_declarative()

sqlalchemy.ext.declarative.as_declarative(**kw)

Class decorator for declarative_base().

Provides a syntactical shortcut to the cls argument sent to declarative_base(), allowing the base class to be converted in-place to a “declarative” base:

from sqlalchemy.ext.declarative import as_declarative

@as_declarative()
class Base(object):
    @declared_attr
    def __tablename__(cls):
        return cls.__name__.lower()
    id = Column(Integer, primary_key=True)

class MyMappedClass(Base):
    # ...

All keyword arguments passed to as_declarative() are passed along to declarative_base().

New in version 0.8.3.

class sqlalchemy.ext.declarative.declared_attr(fget, *arg, **kw)

Bases: sqlalchemy.orm.base._MappedAttribute, __builtin__.property

Mark a class-level method as representing the definition of a mapped property or special declarative member name.

@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__}

Changed in version 0.8: declared_attr can be used with non-ORM or extension attributes, such as user-defined attributes or association_proxy() objects, which will be assigned to the class at class construction time.

sqlalchemy.ext.declarative.api._declarative_constructor(self, **kwargs)

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.

sqlalchemy.ext.declarative.has_inherited_table(cls)

Given a class, return True if any of the classes it inherits from has a mapped table, otherwise return False.

sqlalchemy.ext.declarative.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))
sqlalchemy.ext.declarative.comparable_using(comparator_factory)

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)
sqlalchemy.ext.declarative.instrument_declarative(cls, registry, metadata)

Given a class, configure the class declaratively, using the given registry, which can be any dictionary, and MetaData object.

class sqlalchemy.ext.declarative.AbstractConcreteBase

Bases: sqlalchemy.ext.declarative.api.ConcreteBase

A helper class for ‘concrete’ declarative mappings.

AbstractConcreteBase will use the polymorphic_union() function automatically, against all tables mapped as a subclass to this class. The function is called via the __declare_last__() function, which is essentially a hook for the after_configured() event.

AbstractConcreteBase does not produce a mapped table for the class itself. Compare to ConcreteBase, which does.

Example:

from sqlalchemy.ext.declarative import AbstractConcreteBase

class Employee(AbstractConcreteBase, Base):
    pass

class Manager(Employee):
    __tablename__ = 'manager'
    employee_id = Column(Integer, primary_key=True)
    name = Column(String(50))
    manager_data = Column(String(40))
    __mapper_args__ = {
                    'polymorphic_identity':'manager',
                    'concrete':True}
class sqlalchemy.ext.declarative.ConcreteBase

A helper class for ‘concrete’ declarative mappings.

ConcreteBase will use the polymorphic_union() function automatically, against all tables mapped as a subclass to this class. The function is called via the __declare_last__() function, which is essentially a hook for the after_configured() event.

ConcreteBase produces a mapped table for the class itself. Compare to AbstractConcreteBase, which does not.

Example:

from sqlalchemy.ext.declarative import ConcreteBase

class Employee(ConcreteBase, Base):
    __tablename__ = 'employee'
    employee_id = Column(Integer, primary_key=True)
    name = Column(String(50))
    __mapper_args__ = {
                    'polymorphic_identity':'employee',
                    'concrete':True}

class Manager(Employee):
    __tablename__ = 'manager'
    employee_id = Column(Integer, primary_key=True)
    name = Column(String(50))
    manager_data = Column(String(40))
    __mapper_args__ = {
                    'polymorphic_identity':'manager',
                    'concrete':True}
class sqlalchemy.ext.declarative.DeferredReflection

A helper class for construction of mappings based on a deferred reflection step.

Normally, declarative can be used with reflection by setting a Table object using autoload=True as the __table__ attribute on a declarative class. The caveat is that the Table must be fully reflected, or at the very least have a primary key column, at the point at which a normal declarative mapping is constructed, meaning the Engine must be available at class declaration time.

The DeferredReflection mixin moves the construction of mappers to be at a later point, after a specific method is called which first reflects all Table objects created so far. Classes can define it as such:

from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.ext.declarative import DeferredReflection
Base = declarative_base()

class MyClass(DeferredReflection, Base):
    __tablename__ = 'mytable'

Above, MyClass is not yet mapped. After a series of classes have been defined in the above fashion, all tables can be reflected and mappings created using prepare():

engine = create_engine("someengine://...")
DeferredReflection.prepare(engine)

The DeferredReflection mixin can be applied to individual classes, used as the base for the declarative base itself, or used in a custom abstract class. Using an abstract base allows that only a subset of classes to be prepared for a particular prepare step, which is necessary for applications that use more than one engine. For example, if an application has two engines, you might use two bases, and prepare each separately, e.g.:

class ReflectedOne(DeferredReflection, Base):
    __abstract__ = True

class ReflectedTwo(DeferredReflection, Base):
    __abstract__ = True

class MyClass(ReflectedOne):
    __tablename__ = 'mytable'

class MyOtherClass(ReflectedOne):
    __tablename__ = 'myothertable'

class YetAnotherClass(ReflectedTwo):
    __tablename__ = 'yetanothertable'

# ... etc.

Above, the class hierarchies for ReflectedOne and ReflectedTwo can be configured separately:

ReflectedOne.prepare(engine_one)
ReflectedTwo.prepare(engine_two)

New in version 0.8.

classmethod prepare(engine)

Reflect all Table objects for all current DeferredReflection subclasses