Release: 1.4.46 | Release Date: January 3, 2023

# SQLAlchemy 1.4 Documentation

# 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:

- Using a Generated Base Class - declarative mapping using a base class generated by the `registry` object.

- Declarative Mapping using a Decorator (no declarative base) - declarative mapping using a decorator, rather than a base class.

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`.

New 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`

[1]

When running under Python 2, a Python 2 “old style” class is the only kind of class that isn’t compatible. When running code on Python 2, all classes must extend from the Python `object` class. Under Python 3 this is always the case.

[2]

There is a legacy feature known as a “non primary mapper”, where additional `Mapper` objects may be associated with a class that’s already mapped, however they don’t apply instrumentation to the class. This feature is deprecated as of SQLAlchemy 1.3.