Release: 1.4.0b1 beta release | Release Date: November 2, 2020

SQLAlchemy 1.4 Documentation

Mapper Configuration with Declarative

The section Mapper Configuration Overview discusses the general configurational elements of a Mapper construct, which is the structure that defines how a particular user defined class is mapped to a database table or other SQL construct. The following sections describe specific details about how the declarative system goes about constructing the Mapper.

Defining Mapped Properties with Declarative

The examples given at Table Configuration with Declarative illustrate mappings against table-bound columns; the mapping of an individual column to an ORM class attribute is represented internally by the ColumnProperty construct. There are many other varieties of mapper properties, the most common being the relationship() construct. Other kinds of properties include synonyms to columns which are defined using the synonym() construct, SQL expressions that are defined using the column_property() construct, and deferred columns and SQL expressions which load only when accessed, defined using the deferred() construct.

While an imperative mapping makes use of the properties dictionary to establish all the mapped class attributes, in the declarative mapping, these properties are all specified inline with the class definition, which in the case of a declarative table mapping are inline with the Column objects that will be used to generate a Table object.

Working with the example mapping of User and Address, we may illustrate a declarative table mapping that includes not just Column objects but also relationships and SQL expressions:

# mapping attributes using declarative with declarative table
# i.e. __tablename__

from sqlalchemy import Column, Integer, String, Text, ForeignKey
from sqlalchemy.orm import column_property, relationship, deferred
from sqlalchemy.orm import declarative_base

Base = declarative_base()

class User(Base):
    __tablename__ = 'user'

    id = Column(Integer, primary_key=True)
    name = Column(String)
    firstname = Column(String(50))
    lastname = Column(String(50))

    fullname = column_property(firstname + " " + lastname)

    addresses = relationship("Address", back_populates="user")

class Address(Base):
    __tablename__ = 'address'

    id = Column(Integer, primary_key=True)
    user_id = Column(ForeignKey("user.id"))
    email_address = Column(String)
    address_statistics = deferred(Column(Text))

    user = relationship("User", back_populates="addresses")

The above declarative table mapping features two tables, each with a relationship() referring to the other, as well as a simple SQL expression mapped by column_property(), and an additional Column that will be loaded on a “deferred” basis as defined by the deferred() construct. More documentation on these particular concepts may be found at Basic Relationship Patterns, Using column_property, and Deferred Column Loading.

Properties may be specified with a declarative mapping as above using “hybrid table” style as well; the Column objects that are directly part of a table move into the Table definition but everything else, including composed SQL expressions, would still be inline with the class definition. Constructs that need to refer to a Column directly would reference it in terms of the Table object. To illustrate the above mapping using hybrid table style:

# mapping attributes using declarative with imperative table
# i.e. __table__

from sqlalchemy import Table
from sqlalchemy import Column, Integer, String, Text, ForeignKey
from sqlalchemy.orm import column_property, relationship, deferred
from sqlalchemy.orm import declarative_base

Base = declarative_base()

class User(Base):
    __table__ = Table(
        "user",
        Base.metadata,
        Column("id", Integer, primary_key=True),
        Column("name", String),
        Column("firstname", String(50)),
        Column("lastname", String(50))
    )

    fullname = column_property(__table__.c.firstname + " " + __table__.c.lastname)

    addresses = relationship("Address", back_populates="user")

class Address(Base):
    __table__ = Table(
        "address",
        Base.metadata,
        Column("id", Integer, primary_key=True),
        Column("user_id", ForeignKey("user.id")),
        Column("email_address", String),
        Column("address_statistics", Text)
    )

    address_statistics = deferred(__table__.c.address_statistics)

    user = relationship("User", back_populates="addresses")

Things to note above:

  • The address Table contains a column called address_statistics, however we re-map this column under the same attribute name to be under the control of a deferred() construct.

  • With both declararative table and hybrid table mappings, when we define a ForeignKey construct, we always name the target table using the table name, and not the mapped class name.

  • When we define relationship() constructs, as these constructs create a linkage between two mapped classes where one necessarily is defined before the other, we can refer to the remote class using its string name. This functionality also extends into the area of other arguments specified on the relationship() such as the “primary join” and “order by” arguments. See the section Late-Evaluation of Relationship Arguments for details on this.

Mapper Configuration Options with Declarative

With all mapping forms, the mapping of the class is configured through parameters that become part of the Mapper object. The function which ultimately receives these arguments is the mapper() function, and are delivered to it from one of the front-facing mapping functions defined on the registry object.

For the declarative form of mapping, mapper arguments are specified using the __mapper_args__ declarative class variable, which is a dictionary that is passed as keyword arguments to the mapper() function. Some examples:

Version ID Column

The mapper.version_id_col and mapper.version_id_generator parameters:

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()
    }

Single Table Inheritance

The mapper.polymorphic_on and mapper.polymorphic_identity parameters:

class Person(Base):
    __tablename__ = 'person'

    person_id = Column(Integer, primary_key=True)
    type = Column(String, nullable=False)

    __mapper_args__ = dict(
        polymorphic_on=type,
        polymorphic_identity="person"
    )

class Employee(Person):
    __mapper_args__ = dict(
        polymorphic_identity="employee"
    )

The __mapper_args__ dictionary may be generated from a class-bound descriptor method rather than from a fixed dictionary by making use of the declared_attr() construct. The section Composing Mapped Hierarchies with Mixins discusses this concept further.

Other Declarative Mapping 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

__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)

__table_cls__

Allows the callable / class used to generate a Table to be customized. This is a very open-ended hook that can allow special customizations to a Table that one generates here:

class MyMixin(object):
    @classmethod
    def __table_cls__(cls, name, metadata, *arg, **kw):
        return Table(
            "my_" + name,
            metadata, *arg, **kw
        )

The above mixin would cause all Table objects generated to include the prefix "my_", followed by the name normally specified using the __tablename__ attribute.

__table_cls__ also supports the case of returning None, which causes the class to be considered as single-table inheritance vs. its subclass. This may be useful in some customization schemes to determine that single-table inheritance should take place based on the arguments for the table itself, such as, define as single-inheritance if there is no primary key present:

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

    @classmethod
    def __table_cls__(cls, *arg, **kw):
        for obj in arg[1:]:
            if (isinstance(obj, Column) and obj.primary_key) or \
                    isinstance(obj, PrimaryKeyConstraint):
                return Table(*arg, **kw)

        return None

class Person(AutoTable, Base):
    id = Column(Integer, primary_key=True)

class Employee(Person):
    employee_name = Column(String)

The above Employee class would be mapped as single-table inheritance against Person; the employee_name column would be added as a member of the Person table.

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