Declarative — SQLAlchemy 0.7.1 Documentation

# SQLAlchemy 0.7.1 Documentation

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Version: 0.7.1 Last Updated: 06/05/2011 13:13:59

# 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)

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)

## 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")

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)

## 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)

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

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 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 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.

## 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, 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

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 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()`:

    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}

## Mixin Classes

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.

### 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(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)

### 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(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__
            )

### Mixing in deferred(), column_property(), etc.

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"

### 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(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'}

### 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:
        __table_args__ = {'mysql_engine':'InnoDB'}

    class MyOtherMixin:
        __table_args__ = {'info':'foo'}

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

## 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 0x44693f0>, metaclass=<class 'sqlalchemy.ext.declarative.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. metaclass – Defaults to `DeclarativeMeta`. A metaclass or __metaclass__ compatible callable to use as the meta type of the generated declarative base class.

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

  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__}

`sqlalchemy.ext.declarative.` `_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.