ORM Mapped Class Overview

Overview of ORM class mapping configuration.

For readers new to the SQLAlchemy ORM and/or new to Python in general, it’s recommended to browse through the ORM Quick Start and preferably to work through the SQLAlchemy 1.4 / 2.0 Tutorial, where ORM configuration is first introduced at Defining Table Metadata with the ORM.

ORM Mapping Styles

SQLAlchemy features two distinct styles of mapper configuration, which then feature further sub-options for how they are set up. The variability in mapper styles is present to suit a varied list of developer preferences, including the degree of abstraction of a user-defined class from how it is to be mapped to relational schema tables and columns, what kinds of class hierarchies are in use, including whether or not custom metaclass schemes are present, and finally if there are other class-instrumentation approaches present such as if Python dataclasses are in use simultaneously.

In modern SQLAlchemy, the difference between these styles is mostly superficial; when a particular SQLAlchemy configurational style is used to express the intent to map a class, the internal process of mapping the class proceeds in mostly the same way for each, where the end result is always a user-defined class that has a Mapper configured against a selectable unit, typically represented by a Table object, and the class itself has been instrumented to include behaviors linked to relational operations both at the level of the class as well as on instances of that class. As the process is basically the same in all cases, classes mapped from different styles are always fully interoperable with each other.

The original mapping API is commonly referred to as “classical” style, whereas the more automated style of mapping is known as “declarative” style. SQLAlchemy now refers to these two mapping styles as imperative mapping and declarative mapping.

Regardless of what style of mapping used, all ORM mappings as of SQLAlchemy 1.4 originate from a single object known as registry, which is a registry of mapped classes. Using this registry, a set of mapper configurations can be finalized as a group, and classes within a particular registry may refer to each other by name within the configurational process.

Changed in version 1.4: Declarative and classical mapping are now referred to as “declarative” and “imperative” mapping, and are unified internally, all originating from the registry construct that represents a collection of related mappings.

Declarative Mapping

The Declarative Mapping is the typical way that mappings are constructed in modern SQLAlchemy. The most common pattern is to first construct a base class using the declarative_base() function, which will apply the declarative mapping process to all subclasses that derive from it. Below features a declarative base which is then used in a declarative table mapping:

from sqlalchemy import Column, Integer, String, ForeignKey from sqlalchemy.orm import declarative_base # declarative base class Base = declarative_base() # an example mapping using the base class User(Base): __tablename__ = "user" id = Column(Integer, primary_key=True) name = Column(String) fullname = Column(String) nickname = Column(String)

Above, the declarative_base() callable returns a new base class from which new classes to be mapped may inherit from, as above a new mapped class User is constructed.

The base class refers to a registry object that maintains a collection of related mapped classes. The declarative_base() function is in fact shorthand for first creating the registry with the registry constructor, and then generating a base class using the registry.generate_base() method:

from sqlalchemy.orm import registry # equivalent to Base = declarative_base() mapper_registry = registry() Base = mapper_registry.generate_base()

The major Declarative mapping styles are further detailed in the following sections:

Within the scope of a Declarative mapped class, there are also two varieties of how the Table metadata may be declared. These include:

  • Declarative Table - individual Column definitions are combined with a table name and additional arguments, where the Declarative mapping process will construct a Table object to be mapped.

  • Declarative with Imperative Table (a.k.a. Hybrid Declarative) - Instead of specifying table name and attributes separately, an explicitly constructed Table object is associated with a class that is otherwise mapped declaratively. This style of mapping is a hybrid of “declarative” and “imperative” mapping.

Documentation for Declarative mapping continues at Mapping Classes with Declarative.

Imperative Mapping

An imperative or classical mapping refers to the configuration of a mapped class using the registry.map_imperatively() method, where the target class does not include any declarative class attributes. The “map imperative” style has historically been achieved using the mapper() function directly, however this function now expects that a sqlalchemy.orm.registry() is present.

Deprecated since version 1.4: Using the mapper() function directly to achieve a classical mapping directly is deprecated. The registry.map_imperatively() method retains the identical functionality while also allowing for string-based resolution of other mapped classes from within the registry.

In “classical” form, the table metadata is created separately with the Table construct, then associated with the User class via the registry.map_imperatively() method:

from sqlalchemy import Table, Column, Integer, String, ForeignKey from sqlalchemy.orm import registry mapper_registry = registry() user_table = Table( "user", mapper_registry.metadata, Column("id", Integer, primary_key=True), Column("name", String(50)), Column("fullname", String(50)), Column("nickname", String(12)), ) class User: pass mapper_registry.map_imperatively(User, user_table)

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_obj, Column("id", Integer, primary_key=True), Column("user_id", Integer, ForeignKey("user.id")), Column("email_address", String(50)), ) mapper_registry.map_imperatively( User, user, properties={ "addresses": relationship(Address, backref="user", order_by=address.c.id) }, ) mapper_registry.map_imperatively(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.

Mapped Class Essential Components

With all mapping forms, the mapping of the class can be configured in many ways by passing construction arguments that become part of the Mapper object. The function which ultimately receives these arguments is the mapper() function, which are delivered to it originating from one of the front-facing mapping functions defined on the registry object.

There are four general classes of configuration information that the mapper() function looks for:

The class to be mapped

This is a class that we construct in our application. There are generally no restrictions on the structure of this class. [1] When a Python class is mapped, there can only be one Mapper object for the class. [2]

When mapping with the declarative mapping style, the class to be mapped is either a subclass of the declarative base class, or is handled by a decorator or function such as registry.mapped().

When mapping with the imperative style, the class is passed directly as the map_imperatively.class_ argument.

The table, or other from clause object

In the vast majority of common cases this is an instance of Table. For more advanced use cases, it may also refer to any kind of FromClause object, the most common alternative objects being the Subquery and Join object.

When mapping with the declarative mapping style, the subject table is either generated by the declarative system based on the __tablename__ attribute and the Column objects presented, or it is established via the __table__ attribute. These two styles of configuration are presented at Declarative Table and Declarative with Imperative Table (a.k.a. Hybrid Declarative).

When mapping with the imperative style, the subject table is passed positionally as the map_imperatively.local_table argument.

In contrast to the “one mapper per class” requirement of a mapped class, the Table or other FromClause object that is the subject of the mapping may be associated with any number of mappings. The Mapper applies modifications directly to the user-defined class, but does not modify the given Table or other FromClause in any way.

The properties dictionary

This is a dictionary of all of the attributes that will be associated with the mapped class. By default, the Mapper generates entries for this dictionary derived from the given Table, in the form of ColumnProperty objects which each refer to an individual Column of the mapped table. The properties dictionary will also contain all the other kinds of MapperProperty objects to be configured, most commonly instances generated by the relationship() construct.

When mapping with the declarative mapping style, the properties dictionary is generated by the declarative system by scanning the class to be mapped for appropriate attributes. See the section Defining Mapped Properties with Declarative for notes on this process.

When mapping with the imperative style, the properties dictionary is passed directly as the properties argument to registry.map_imperatively(), which will pass it along to the mapper.properties parameter.

Other mapper configuration parameters

When mapping with the declarative mapping style, additional mapper configuration arguments are configured via the __mapper_args__ class attribute. Examples of use are available at Mapper Configuration Options with Declarative.

When mapping with the imperative style, keyword arguments are passed to the to registry.map_imperatively() method which passes them along to the mapper() function.

The full range of parameters accepted are documented at mapper.

Mapped Class Behavior

Across all styles of mapping using the registry object, the following behaviors are common:

Default Constructor

The registry applies a default constructor, i.e. __init__ method, to all mapped classes that don’t explicitly have their own __init__ method. The behavior of this method is such that it provides a convenient keyword constructor that will accept as optional keyword arguments all the attributes that are named. E.g.:

from sqlalchemy.orm import declarative_base Base = declarative_base() class User(Base): __tablename__ = "user" id = Column(...) name = Column(...) fullname = Column(...)

An object of type User above will have a constructor which allows User objects to be created as:

u1 = User(name="some name", fullname="some fullname")

The above constructor may be customized by passing a Python callable to the registry.constructor parameter which provides the desired default __init__() behavior.

The constructor also applies to imperative mappings:

from sqlalchemy.orm import registry mapper_registry = registry() user_table = Table( "user", mapper_registry.metadata, Column("id", Integer, primary_key=True), Column("name", String(50)), ) class User: pass mapper_registry.map_imperatively(User, user_table)

The above class, mapped imperatively as described at Imperative Mapping, will also feature the default constructor associated with the registry.

Added in version 1.4: classical mappings now support a standard configuration-level constructor when they are mapped via the registry.map_imperatively() method.

Runtime Introspection of Mapped classes, Instances and Mappers

A class that is mapped using registry will also feature a few attributes that are common to all mappings:

  • The __mapper__ attribute will refer to the Mapper that is associated with the class:

    mapper = User.__mapper__

    This Mapper is also what’s returned when using the inspect() function against the mapped class:

    from sqlalchemy import inspect mapper = inspect(User)

  • The __table__ attribute will refer to the Table, or more generically to the FromClause object, to which the class is mapped:

    table = User.__table__

    This FromClause is also what’s returned when using the Mapper.local_table attribute of the Mapper:

    table = inspect(User).local_table

    For a single-table inheritance mapping, where the class is a subclass that does not have a table of its own, the Mapper.local_table attribute as well as the .__table__ attribute will be None. To retrieve the “selectable” that is actually selected from during a query for this class, this is available via the Mapper.selectable attribute:

    table = inspect(User).selectable

Inspection of Mapper objects

As illustrated in the previous section, 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

Mapper

Inspection of Mapped Instances

The inspect() function also provides information about instances of a mapped class. When applied to an instance of a mapped class, rather than the class itself, the object returned is known as InstanceState, which will provide links to not only the Mapper in use by the class, but also a detailed interface that provides information on the state of individual attributes within the instance including their current value and how this relates to what their database-loaded value is.

Given an instance of the User class loaded from the database:

>>> u1 = session.scalars(select(User)).first()

The inspect() function will return to us an InstanceState object:

>>> insp = inspect(u1) >>> insp <sqlalchemy.orm.state.InstanceState object at 0x7f07e5fec2e0>

With this object we can see elements such as the Mapper:

>>> insp.mapper <Mapper at 0x7f07e614ef50; User>

The Session to which the object is attached, if any:

>>> insp.session <sqlalchemy.orm.session.Session object at 0x7f07e614f160>

Information about the current persistence state for the object:

>>> insp.persistent True >>> insp.pending False

Attribute state information such as attributes that have not been loaded or lazy loaded (assume addresses refers to a relationship() on the mapped class to a related class):

>>> insp.unloaded {'addresses'}

Information regarding the current in-Python status of attributes, such as attributes that have not been modified since the last flush:

>>> insp.unmodified {'nickname', 'name', 'fullname', 'id'}

as well as specific history on modifications to attributes since the last flush:

>>> insp.attrs.nickname.value 'nickname' >>> u1.nickname = "new nickname" >>> insp.attrs.nickname.history History(added=['new nickname'], unchanged=(), deleted=['nickname'])

See also

InstanceState

InstanceState.attrs

AttributeState