Types of Mappings

Modern SQLAlchemy features two distinct styles of mapper configuration. The “Classical” style is SQLAlchemy’s original mapping API, whereas “Declarative” is the richer and more succinct system that builds on top of “Classical”. Both styles may be used interchangeably, as the end result of each is exactly the same - a user-defined class mapped by the mapper() function onto a selectable unit, typically a Table.

Declarative Mapping

The Declarative Mapping is the typical way that mappings are constructed in modern SQLAlchemy. Making use of the Declarative system, the components of the user-defined class as well as the Table metadata to which the class is mapped are defined at once:

from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String, ForeignKey

Base = declarative_base()

class User(Base):
    __tablename__ = 'user'

    id = Column(Integer, primary_key=True)
    name = Column(String)
    fullname = Column(String)
    nickname = Column(String)

Above, a basic single-table mapping with four columns. Additional attributes, such as relationships to other mapped classes, are also declared inline within the class definition:

class User(Base):
    __tablename__ = 'user'

    id = Column(Integer, primary_key=True)
    name = Column(String)
    fullname = Column(String)
    nickname = Column(String)

    addresses = relationship("Address", backref="user", order_by="Address.id")

class Address(Base):
    __tablename__ = 'address'

    id = Column(Integer, primary_key=True)
    user_id = Column(ForeignKey('user.id'))
    email_address = Column(String)

The declarative mapping system is introduced in the Object Relational Tutorial. For additional details on how this system works, see Declarative.

Classical Mappings

A Classical Mapping refers to the configuration of a mapped class using the mapper() function, without using the Declarative system. This is SQLAlchemy’s original class mapping API, and is still the base mapping system provided by the ORM.

In “classical” form, the table metadata is created separately with the Table construct, then associated with the User class via the mapper() function:

from sqlalchemy import Table, MetaData, Column, Integer, String, ForeignKey
from sqlalchemy.orm import mapper

metadata = MetaData()

user = Table('user', metadata,
            Column('id', Integer, primary_key=True),
            Column('name', String(50)),
            Column('fullname', String(50)),
            Column('nickname', String(12))
        )

class User(object):
    def __init__(self, name, fullname, nickname):
        self.name = name
        self.fullname = fullname
        self.nickname = nickname

mapper(User, user)

Information about mapped attributes, such as relationships to other classes, are provided via the properties dictionary. The example below illustrates a second Table object, mapped to a class called Address, then linked to User via relationship():

address = Table('address', metadata,
            Column('id', Integer, primary_key=True),
            Column('user_id', Integer, ForeignKey('user.id')),
            Column('email_address', String(50))
            )

mapper(User, user, properties={
    'addresses' : relationship(Address, backref='user', order_by=address.c.id)
})

mapper(Address, address)

When using classical mappings, classes must be provided directly without the benefit of the “string lookup” system provided by Declarative. SQL expressions are typically specified in terms of the Table objects, i.e. address.c.id above for the Address relationship, and not Address.id, as Address may not yet be linked to table metadata, nor can we specify a string here.

Some examples in the documentation still use the classical approach, but note that the classical as well as Declarative approaches are fully interchangeable. Both systems ultimately create the same configuration, consisting of a Table, user-defined class, linked together with a mapper(). When we talk about “the behavior of mapper()”, this includes when using the Declarative system as well - it’s still used, just behind the scenes.

Runtime Introspection of Mappings, Objects

The Mapper object is available from any mapped class, regardless of method, using the Runtime Inspection API system. Using the inspect() function, one can acquire the Mapper from a mapped class:

>>> from sqlalchemy import inspect
>>> insp = inspect(User)

Detailed information is available including Mapper.columns:

>>> insp.columns
<sqlalchemy.util._collections.OrderedProperties object at 0x102f407f8>

This is a namespace that can be viewed in a list format or via individual names:

>>> list(insp.columns)
[Column('id', Integer(), table=<user>, primary_key=True, nullable=False), Column('name', String(length=50), table=<user>), Column('fullname', String(length=50), table=<user>), Column('nickname', String(length=50), table=<user>)]
>>> insp.columns.name
Column('name', String(length=50), table=<user>)

Other namespaces include Mapper.all_orm_descriptors, which includes all mapped attributes as well as hybrids, association proxies:

>>> insp.all_orm_descriptors
<sqlalchemy.util._collections.ImmutableProperties object at 0x1040e2c68>
>>> insp.all_orm_descriptors.keys()
['fullname', 'nickname', 'name', 'id']

As well as Mapper.column_attrs:

>>> list(insp.column_attrs)
[<ColumnProperty at 0x10403fde0; id>, <ColumnProperty at 0x10403fce8; name>, <ColumnProperty at 0x1040e9050; fullname>, <ColumnProperty at 0x1040e9148; nickname>]
>>> insp.column_attrs.name
<ColumnProperty at 0x10403fce8; name>
>>> insp.column_attrs.name.expression
Column('name', String(length=50), table=<user>)

See also

Runtime Inspection API

Mapper

InstanceState