Class Mapping API

Object Name	Description
add_mapped_attribute(target, key, attr)	Add a new mapped attribute to an ORM mapped class.
as_declarative(**kw)	Class decorator which will adapt a given class into a declarative_base().
class_mapper(class_[, configure])	Given a class, return the primary Mapper associated with the key.
clear_mappers()	Remove all mappers from all classes.
column_property(column, *additional_columns, [group, deferred, raiseload, comparator_factory, init, repr, default, default_factory, compare, kw_only, active_history, expire_on_flush, info, doc])	Provide a column-level property for use with a mapping.
configure_mappers()	Initialize the inter-mapper relationships of all mappers that have been constructed thus far across all registry collections.
declarative_base(*, [metadata, mapper, cls, name, class_registry, type_annotation_map, constructor, metaclass])	Construct a base class for declarative class definitions.
declarative_mixin(cls)	Mark a class as providing the feature of “declarative mixin”.
DeclarativeBase	Base class used for declarative class definitions.
DeclarativeBaseNoMeta	Same as DeclarativeBase, but does not use a metaclass to intercept new attributes.
declared_attr	Mark a class-level method as representing the definition of a mapped property or Declarative directive.
has_inherited_table(cls)	Given a class, return True if any of the classes it inherits from has a mapped table, otherwise return False.
identity_key([class_, ident], *, [instance, row, identity_token])	Generate “identity key” tuples, as are used as keys in the Session.identity_map dictionary.
mapped_column([__name_pos, __type_pos], *args, [init, repr, default, default_factory, compare, kw_only, nullable, primary_key, deferred, deferred_group, deferred_raiseload, use_existing_column, name, type_, autoincrement, doc, key, index, unique, info, onupdate, insert_default, server_default, server_onupdate, active_history, quote, system, comment, sort_order], **kw)	declare a new ORM-mapped Column construct for use within Declarative Table configuration.
MappedAsDataclass	Mixin class to indicate when mapping this class, also convert it to be a dataclass.
MappedClassProtocol	A protocol representing a SQLAlchemy mapped class.
Mapper	Defines an association between a Python class and a database table or other relational structure, so that ORM operations against the class may proceed.
object_mapper(instance)	Given an object, return the primary Mapper associated with the object instance.
orm_insert_sentinel([name, type_], *, [default, omit_from_statements])	Provides a surrogate mapped_column() that generates a so-called sentinel column, allowing efficient bulk inserts with deterministic RETURNING sorting for tables that don’t otherwise have qualifying primary key configurations.
polymorphic_union(table_map, typecolname[, aliasname, cast_nulls])	Create a UNION statement used by a polymorphic mapper.
reconstructor(fn)	Decorate a method as the ‘reconstructor’ hook.
registry	Generalized registry for mapping classes.
synonym_for(name[, map_column])	Decorator that produces an synonym() attribute in conjunction with a Python descriptor.

class sqlalchemy.orm.registry

Generalized registry for mapping classes.

The registry serves as the basis for maintaining a collection of mappings, and provides configurational hooks used to map classes.

The three general kinds of mappings supported are Declarative Base, Declarative Decorator, and Imperative Mapping. All of these mapping styles may be used interchangeably:

• registry.generate_base() returns a new declarative base class, and is the underlying implementation of the declarative_base() function.

• registry.mapped() provides a class decorator that will apply declarative mapping to a class without the use of a declarative base class.

• registry.map_imperatively() will produce a Mapper for a class without scanning the class for declarative class attributes. This method suits the use case historically provided by the sqlalchemy.orm.mapper() classical mapping function, which is removed as of SQLAlchemy 2.0.

New in version 1.4.

Members

__init__(), as_declarative_base(), configure(), dispose(), generate_base(), map_declaratively(), map_imperatively(), mapped(), mapped_as_dataclass(), mappers, update_type_annotation_map()

See also

ORM Mapped Class Overview - overview of class mapping styles.

method sqlalchemy.orm.registry.__init__(*, metadata: Optional[MetaData] = None, class_registry: Optional[clsregistry._ClsRegistryType] = None, type_annotation_map: Optional[_TypeAnnotationMapType] = None, constructor: Callable[..., None] = <function _declarative_constructor>)

Construct a new registry

Parameters:

• metadata – An optional MetaData instance. All Table objects generated using declarative table mapping will make use of this MetaData collection. If this argument is left at its default of None, a blank MetaData collection is created.

• constructor – Specify the implementation for the __init__ function on a mapped class that has no __init__ of its own. Defaults to an 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.

• class_registry – optional dictionary that will serve as the registry of class names-> mapped classes when string names are used to identify classes inside of relationship() and others. Allows two or more declarative base classes to share the same registry of class names for simplified inter-base relationships.

• type_annotation_map –

  optional dictionary of Python types to SQLAlchemy TypeEngine classes or instances. The provided dict will update the default type mapping. This is used exclusively by the MappedColumn construct to produce column types based on annotations within the Mapped type.

  New in version 2.0.

  See also

  Customizing the Type Map

method sqlalchemy.orm.registry.as_declarative_base(**kw: Any) → Callable[[Type[_T]], Type[_T]]

Class decorator which will invoke registry.generate_base() for a given base class.

E.g.:

from sqlalchemy.orm import registry

mapper_registry = registry()

@mapper_registry.as_declarative_base()
class Base:
    @declared_attr
    def __tablename__(cls):
        return cls.__name__.lower()
    id = Column(Integer, primary_key=True)

class MyMappedClass(Base):
    # ...

All keyword arguments passed to registry.as_declarative_base() are passed along to registry.generate_base().

method sqlalchemy.orm.registry.configure(cascade: bool = False) → None

Configure all as-yet unconfigured mappers in this registry.

The configure step is used to reconcile and initialize the relationship() linkages between mapped classes, as well as to invoke configuration events such as the MapperEvents.before_configured() and MapperEvents.after_configured(), which may be used by ORM extensions or user-defined extension hooks.

If one or more mappers in this registry contain relationship() constructs that refer to mapped classes in other registries, this registry is said to be dependent on those registries. In order to configure those dependent registries automatically, the configure.cascade flag should be set to True. Otherwise, if they are not configured, an exception will be raised. The rationale behind this behavior is to allow an application to programmatically invoke configuration of registries while controlling whether or not the process implicitly reaches other registries.

As an alternative to invoking registry.configure(), the ORM function configure_mappers() function may be used to ensure configuration is complete for all registry objects in memory. This is generally simpler to use and also predates the usage of registry objects overall. However, this function will impact all mappings throughout the running Python process and may be more memory/time consuming for an application that has many registries in use for different purposes that may not be needed immediately.

See also

configure_mappers()

New in version 1.4.0b2.

method sqlalchemy.orm.registry.dispose(cascade: bool = False) → None

Dispose of all mappers in this registry.

After invocation, all the classes that were mapped within this registry will no longer have class instrumentation associated with them. This method is the per-registry analogue to the application-wide clear_mappers() function.

If this registry contains mappers that are dependencies of other registries, typically via relationship() links, then those registries must be disposed as well. When such registries exist in relation to this one, their registry.dispose() method will also be called, if the dispose.cascade flag is set to True; otherwise, an error is raised if those registries were not already disposed.

New in version 1.4.0b2.

See also

clear_mappers()

method sqlalchemy.orm.registry.generate_base(mapper: ~typing.Callable[[...], ~sqlalchemy.orm.mapper.Mapper[~typing.Any]] | None = None, cls: ~typing.Type[~typing.Any] = <class 'object'>, name: str = 'Base', metaclass: ~typing.Type[~typing.Any] = <class 'sqlalchemy.orm.decl_api.DeclarativeMeta'>) → Any

Generate a declarative base class.

Classes that inherit from the returned class object will be automatically mapped using declarative mapping.

E.g.:

from sqlalchemy.orm import registry

mapper_registry = registry()

Base = mapper_registry.generate_base()

class MyClass(Base):
    __tablename__ = "my_table"
    id = Column(Integer, primary_key=True)

The above dynamically generated class is equivalent to the non-dynamic example below:

from sqlalchemy.orm import registry
from sqlalchemy.orm.decl_api import DeclarativeMeta

mapper_registry = registry()

class Base(metaclass=DeclarativeMeta):
    __abstract__ = True
    registry = mapper_registry
    metadata = mapper_registry.metadata

    __init__ = mapper_registry.constructor

Changed in version 2.0: Note that the registry.generate_base() method is superseded by the new DeclarativeBase class, which generates a new “base” class using subclassing, rather than return value of a function. This allows an approach that is compatible with PEP 484 typing tools.

The registry.generate_base() method provides the implementation for the declarative_base() function, which creates the registry and base class all at once.

See the section Declarative Mapping for background and examples.

Parameters:

• mapper – An optional callable, defaults to Mapper. This function is used to generate new Mapper objects.

• 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.

• metaclass – Defaults to DeclarativeMeta. A metaclass or __metaclass__ compatible callable to use as the meta type of the generated declarative base class.

See also

Declarative Mapping

declarative_base()

method sqlalchemy.orm.registry.map_declaratively(cls: Type[_O]) → Mapper[_O]

Map a class declaratively.

In this form of mapping, the class is scanned for mapping information, including for columns to be associated with a table, and/or an actual table object.

Returns the Mapper object.

E.g.:

from sqlalchemy.orm import registry

mapper_registry = registry()

class Foo:
    __tablename__ = 'some_table'

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

mapper = mapper_registry.map_declaratively(Foo)

This function is more conveniently invoked indirectly via either the registry.mapped() class decorator or by subclassing a declarative metaclass generated from registry.generate_base().

See the section Declarative Mapping for complete details and examples.

Parameters:

cls – class to be mapped.

Returns:

a Mapper object.

See also

Declarative Mapping

registry.mapped() - more common decorator interface to this function.

registry.map_imperatively()

method sqlalchemy.orm.registry.map_imperatively(class_: Type[_O], local_table: FromClause | None = None, **kw: Any) → Mapper[_O]

Map a class imperatively.

In this form of mapping, the class is not scanned for any mapping information. Instead, all mapping constructs are passed as arguments.

This method is intended to be fully equivalent to the now-removed SQLAlchemy mapper() function, except that it’s in terms of a particular registry.

E.g.:

from sqlalchemy.orm import registry

mapper_registry = registry()

my_table = Table(
    "my_table",
    mapper_registry.metadata,
    Column('id', Integer, primary_key=True)
)

class MyClass:
    pass

mapper_registry.map_imperatively(MyClass, my_table)

See the section Imperative Mapping for complete background and usage examples.

Parameters:

• class_ – The class to be mapped. Corresponds to the Mapper.class_ parameter.

• local_table – the Table or other FromClause object that is the subject of the mapping. Corresponds to the Mapper.local_table parameter.

• **kw – all other keyword arguments are passed to the Mapper constructor directly.

See also

Imperative Mapping

Declarative Mapping

method sqlalchemy.orm.registry.mapped(cls: Type[_O]) → Type[_O]

Class decorator that will apply the Declarative mapping process to a given class.

E.g.:

from sqlalchemy.orm import registry

mapper_registry = registry()

@mapper_registry.mapped
class Foo:
    __tablename__ = 'some_table'

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

See the section Declarative Mapping for complete details and examples.

Parameters:

cls – class to be mapped.

Returns:

the class that was passed.

See also

Declarative Mapping

registry.generate_base() - generates a base class that will apply Declarative mapping to subclasses automatically using a Python metaclass.

See also

registry.mapped_as_dataclass()

method sqlalchemy.orm.registry.mapped_as_dataclass(_registry__cls: Type[_O] | None = None, *, init: _NoArg | bool = _NoArg.NO_ARG, repr: _NoArg | bool = _NoArg.NO_ARG, eq: _NoArg | bool = _NoArg.NO_ARG, order: _NoArg | bool = _NoArg.NO_ARG, unsafe_hash: _NoArg | bool = _NoArg.NO_ARG, match_args: _NoArg | bool = _NoArg.NO_ARG, kw_only: _NoArg | bool = _NoArg.NO_ARG, dataclass_callable: _NoArg | Callable[..., Type[Any]] = _NoArg.NO_ARG) → Type[_O] | Callable[[Type[_O]], Type[_O]]

Class decorator that will apply the Declarative mapping process to a given class, and additionally convert the class to be a Python dataclass.

See also

Declarative Dataclass Mapping - complete background on SQLAlchemy native dataclass mapping

New in version 2.0.

attribute sqlalchemy.orm.registry.mappers

read only collection of all Mapper objects.

method sqlalchemy.orm.registry.update_type_annotation_map(type_annotation_map: _TypeAnnotationMapType) → None

update the registry.type_annotation_map with new values.

function sqlalchemy.orm.add_mapped_attribute(target: Type[_O], key: str, attr: MapperProperty[Any]) → None

Add a new mapped attribute to an ORM mapped class.

E.g.:

add_mapped_attribute(User, "addresses", relationship(Address))

This may be used for ORM mappings that aren’t using a declarative metaclass that intercepts attribute set operations.

New in version 2.0.

function sqlalchemy.orm.column_property(column: _ORMColumnExprArgument[_T], *additional_columns: _ORMColumnExprArgument[Any], group: str | None = None, deferred: bool = False, raiseload: bool = False, comparator_factory: Type[PropComparator[_T]] | None = None, init: _NoArg | bool = _NoArg.NO_ARG, repr: _NoArg | bool = _NoArg.NO_ARG, default: Any | None = _NoArg.NO_ARG, default_factory: _NoArg | Callable[[], _T] = _NoArg.NO_ARG, compare: _NoArg | bool = _NoArg.NO_ARG, kw_only: _NoArg | bool = _NoArg.NO_ARG, active_history: bool = False, expire_on_flush: bool = True, info: _InfoType | None = None, doc: str | None = None) → MappedSQLExpression[_T]

Provide a column-level property for use with a mapping.

With Declarative mappings, column_property() is used to map read-only SQL expressions to a mapped class.

When using Imperative mappings, column_property() also takes on the role of mapping table columns with additional features. When using fully Declarative mappings, the mapped_column() construct should be used for this purpose.

With Declarative Dataclass mappings, column_property() is considered to be read only, and will not be included in the Dataclass __init__() constructor.

The column_property() function returns an instance of ColumnProperty.

See also

Using column_property - general use of column_property() to map SQL expressions

Applying Load, Persistence and Mapping Options for Imperative Table Columns - usage of column_property() with Imperative Table mappings to apply additional options to a plain Column object

Parameters:

• *cols – list of Column objects to be mapped.

• active_history=False – Used only for Imperative Table mappings, or legacy-style Declarative mappings (i.e. which have not been upgraded to mapped_column()), for column-based attributes that are expected to be writeable; use mapped_column() with mapped_column.active_history for Declarative mappings. See that parameter for functional details.

• comparator_factory – a class which extends Comparator which provides custom SQL clause generation for comparison operations.

• group – a group name for this property when marked as deferred.

• deferred – when True, the column property is “deferred”, meaning that it does not load immediately, and is instead loaded when the attribute is first accessed on an instance. See also deferred().

• doc – optional string that will be applied as the doc on the class-bound descriptor.

• expire_on_flush=True – Disable expiry on flush. A column_property() which refers to a SQL expression (and not a single table-bound column) is considered to be a “read only” property; populating it has no effect on the state of data, and it can only return database state. For this reason a column_property()’s value is expired whenever the parent object is involved in a flush, that is, has any kind of “dirty” state within a flush. Setting this parameter to False will have the effect of leaving any existing value present after the flush proceeds. Note that the Session with default expiration settings still expires all attributes after a Session.commit() call, however.

• info – Optional data dictionary which will be populated into the MapperProperty.info attribute of this object.

• raiseload –

  if True, indicates the column should raise an error when undeferred, rather than loading the value. This can be altered at query time by using the deferred() option with raiseload=False.

  New in version 1.4.

  See also

  Using raiseload to prevent deferred column loads

• init –

  Deprecated since version 1.4: The column_property.init parameter is deprecated for column_property(). This parameter applies to a writeable-attribute in a Declarative Dataclasses configuration only, and column_property() is treated as a read-only attribute in this context.

• default –

  Deprecated since version 1.4: The column_property.default parameter is deprecated for column_property(). This parameter applies to a writeable-attribute in a Declarative Dataclasses configuration only, and column_property() is treated as a read-only attribute in this context.

• default_factory –

  Deprecated since version 1.4: The column_property.default_factory parameter is deprecated for column_property(). This parameter applies to a writeable-attribute in a Declarative Dataclasses configuration only, and column_property() is treated as a read-only attribute in this context.

• kw_only –

  Deprecated since version 1.4: The column_property.kw_only parameter is deprecated for column_property(). This parameter applies to a writeable-attribute in a Declarative Dataclasses configuration only, and column_property() is treated as a read-only attribute in this context.

function sqlalchemy.orm.declarative_base(*, metadata: Optional[MetaData] = None, mapper: Optional[Callable[..., Mapper[Any]]] = None, cls: Type[Any] = <class 'object'>, name: str = 'Base', class_registry: Optional[clsregistry._ClsRegistryType] = None, type_annotation_map: Optional[_TypeAnnotationMapType] = None, constructor: Callable[..., None] = <function _declarative_constructor>, metaclass: Type[Any] = <class 'sqlalchemy.orm.decl_api.DeclarativeMeta'>) → Any

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.

Changed in version 2.0: Note that the declarative_base() function is superseded by the new DeclarativeBase class, which generates a new “base” class using subclassing, rather than return value of a function. This allows an approach that is compatible with PEP 484 typing tools.

The declarative_base() function is a shorthand version of using the registry.generate_base() method. That is, the following:

from sqlalchemy.orm import declarative_base

Base = declarative_base()

Is equivalent to:

from sqlalchemy.orm import registry

mapper_registry = registry()
Base = mapper_registry.generate_base()

See the docstring for registry and registry.generate_base() for more details.

Changed in version 1.4: The declarative_base() function is now a specialization of the more generic registry class. The function also moves to the sqlalchemy.orm package from the declarative.ext package.

Parameters:

• 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 – Specify the implementation for the __init__ function on a mapped class that has no __init__ of its own. Defaults to an 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.

• class_registry – optional dictionary that will serve as the registry of class names-> mapped classes when string names are used to identify classes inside of relationship() and others. Allows two or more declarative base classes to share the same registry of class names for simplified inter-base relationships.

• type_annotation_map –

  optional dictionary of Python types to SQLAlchemy TypeEngine classes or instances. This is used exclusively by the MappedColumn construct to produce column types based on annotations within the Mapped type.

  New in version 2.0.

  See also

  Customizing the Type Map

• metaclass – Defaults to DeclarativeMeta. A metaclass or __metaclass__ compatible callable to use as the meta type of the generated declarative base class.

See also

registry

function sqlalchemy.orm.declarative_mixin(cls: Type[_T]) → Type[_T]

Mark a class as providing the feature of “declarative mixin”.

E.g.:

from sqlalchemy.orm import declared_attr
from sqlalchemy.orm import declarative_mixin

@declarative_mixin
class MyMixin:

    @declared_attr
    def __tablename__(cls):
        return cls.__name__.lower()

    __table_args__ = {'mysql_engine': 'InnoDB'}
    __mapper_args__= {'always_refresh': True}

    id =  Column(Integer, primary_key=True)

class MyModel(MyMixin, Base):
    name = Column(String(1000))

The declarative_mixin() decorator currently does not modify the given class in any way; it’s current purpose is strictly to assist the Mypy plugin in being able to identify SQLAlchemy declarative mixin classes when no other context is present.

New in version 1.4.6.

See also

Composing Mapped Hierarchies with Mixins

Using @declared_attr and Declarative Mixins - in the Mypy plugin documentation

function sqlalchemy.orm.as_declarative(**kw: Any) → Callable[[Type[_T]], Type[_T]]

Class decorator which will adapt a given class into a declarative_base().

This function makes use of the registry.as_declarative_base() method, by first creating a registry automatically and then invoking the decorator.

E.g.:

from sqlalchemy.orm import as_declarative

@as_declarative()
class Base:
    @declared_attr
    def __tablename__(cls):
        return cls.__name__.lower()
    id = Column(Integer, primary_key=True)

class MyMappedClass(Base):
    # ...

See also

registry.as_declarative_base()

function sqlalchemy.orm.mapped_column(__name_pos: str | _TypeEngineArgument[Any] | SchemaEventTarget | None = None, __type_pos: _TypeEngineArgument[Any] | SchemaEventTarget | None = None, *args: SchemaEventTarget, init: _NoArg | bool = _NoArg.NO_ARG, repr: _NoArg | bool = _NoArg.NO_ARG, default: Any | None = _NoArg.NO_ARG, default_factory: _NoArg | Callable[[], _T] = _NoArg.NO_ARG, compare: _NoArg | bool = _NoArg.NO_ARG, kw_only: _NoArg | bool = _NoArg.NO_ARG, nullable: bool | Literal[SchemaConst.NULL_UNSPECIFIED] | None = SchemaConst.NULL_UNSPECIFIED, primary_key: bool | None = False, deferred: _NoArg | bool = _NoArg.NO_ARG, deferred_group: str | None = None, deferred_raiseload: bool | None = None, use_existing_column: bool = False, name: str | None = None, type_: _TypeEngineArgument[Any] | None = None, autoincrement: _AutoIncrementType = 'auto', doc: str | None = None, key: str | None = None, index: bool | None = None, unique: bool | None = None, info: _InfoType | None = None, onupdate: Any | None = None, insert_default: Any | None = _NoArg.NO_ARG, server_default: _ServerDefaultArgument | None = None, server_onupdate: FetchedValue | None = None, active_history: bool = False, quote: bool | None = None, system: bool = False, comment: str | None = None, sort_order: _NoArg | int = _NoArg.NO_ARG, **kw: Any) → MappedColumn[Any]

declare a new ORM-mapped Column construct for use within Declarative Table configuration.

The mapped_column() function provides an ORM-aware and Python-typing-compatible construct which is used with declarative mappings to indicate an attribute that’s mapped to a Core Column object. It provides the equivalent feature as mapping an attribute to a Column object directly when using Declarative, specifically when using Declarative Table configuration.

New in version 2.0.

mapped_column() is normally used with explicit typing along with the Mapped annotation type, where it can derive the SQL type and nullability for the column based on what’s present within the Mapped annotation. It also may be used without annotations as a drop-in replacement for how Column is used in Declarative mappings in SQLAlchemy 1.x style.

For usage examples of mapped_column(), see the documentation at Declarative Table with mapped_column().

See also

Declarative Table with mapped_column() - complete documentation

ORM Declarative Models - migration notes for Declarative mappings using 1.x style mappings

Parameters:

• __name – String name to give to the Column. This is an optional, positional only argument that if present must be the first positional argument passed. If omitted, the attribute name to which the mapped_column() is mapped will be used as the SQL column name.

• __type – TypeEngine type or instance which will indicate the datatype to be associated with the Column. This is an optional, positional-only argument that if present must immediately follow the __name parameter if present also, or otherwise be the first positional parameter. If omitted, the ultimate type for the column may be derived either from the annotated type, or if a ForeignKey is present, from the datatype of the referenced column.

• *args – Additional positional arguments include constructs such as ForeignKey, CheckConstraint, and Identity, which are passed through to the constructed Column.

• nullable – Optional bool, whether the column should be “NULL” or “NOT NULL”. If omitted, the nullability is derived from the type annotation based on whether or not typing.Optional is present. nullable defaults to True otherwise for non-primary key columns, and False for primary key columns.

• primary_key – optional bool, indicates the Column would be part of the table’s primary key or not.

• deferred –

  Optional bool - this keyword argument is consumed by the ORM declarative process, and is not part of the Column itself; instead, it indicates that this column should be “deferred” for loading as though mapped by deferred().

  See also

  Configuring Column Deferral on Mappings

• deferred_group –

  Implies mapped_column.deferred to True, and set the deferred.group parameter.

  See also

  Loading deferred columns in groups

• deferred_raiseload –

  Implies mapped_column.deferred to True, and set the deferred.raiseload parameter.

  See also

  Using raiseload to prevent deferred column loads

• use_existing_column –

  if True, will attempt to locate the given column name on an inherited superclass (typically single inheriting superclass), and if present, will not produce a new column, mapping to the superclass column as though it were omitted from this class. This is used for mixins that add new columns to an inherited superclass.

  See also

  Resolving Column Conflicts with use_existing_column

  New in version 2.0.0b4.

• default –

  Passed directly to the Column.default parameter if the mapped_column.insert_default parameter is not present. Additionally, when used with Declarative Dataclass Mapping, indicates a default Python value that should be applied to the keyword constructor within the generated __init__() method.

  Note that in the case of dataclass generation when mapped_column.insert_default is not present, this means the mapped_column.default value is used in two places, both the __init__() method as well as the Column.default parameter. While this behavior may change in a future release, for the moment this tends to “work out”; a default of None will mean that the Column gets no default generator, whereas a default that refers to a non-None Python or SQL expression value will be assigned up front on the object when __init__() is called, which is the same value that the Core Insert construct would use in any case, leading to the same end result.

  Note

  When using Core level column defaults that are callables to be interpreted by the underlying Column in conjunction with ORM-mapped dataclasses, especially those that are context-aware default functions, the mapped_column.insert_default parameter must be used instead. This is necessary to disambiguate the callable from being interpreted as a dataclass level default.

• insert_default – Passed directly to the Column.default parameter; will supersede the value of mapped_column.default when present, however mapped_column.default will always apply to the constructor default for a dataclasses mapping.

• sort_order –

  An integer that indicates how this mapped column should be sorted compared to the others when the ORM is creating a Table. Among mapped columns that have the same value the default ordering is used, placing first the mapped columns defined in the main class, then the ones in the super classes. Defaults to 0. The sort is ascending.

  New in version 2.0.4.

• active_history=False –

  When True, indicates that the “previous” value for a scalar attribute should be loaded when replaced, if not already loaded. Normally, history tracking logic for simple non-primary-key scalar values only needs to be aware of the “new” value in order to perform a flush. This flag is available for applications that make use of get_history() or Session.is_modified() which also need to know the “previous” value of the attribute.

  New in version 2.0.10.

• init – Specific to Declarative Dataclass Mapping, specifies if the mapped attribute should be part of the __init__() method as generated by the dataclass process.

• repr – Specific to Declarative Dataclass Mapping, specifies if the mapped attribute should be part of the __repr__() method as generated by the dataclass process.

• default_factory – Specific to Declarative Dataclass Mapping, specifies a default-value generation function that will take place as part of the __init__() method as generated by the dataclass process.

• compare –

  Specific to Declarative Dataclass Mapping, indicates if this field should be included in comparison operations when generating the __eq__() and __ne__() methods for the mapped class.

  New in version 2.0.0b4.

• kw_only – Specific to Declarative Dataclass Mapping, indicates if this field should be marked as keyword-only when generating the __init__().

• **kw – All remaining keyword arguments are passed through to the constructor for the Column.

class sqlalchemy.orm.declared_attr

Mark a class-level method as representing the definition of a mapped property or Declarative directive.

declared_attr is typically applied as a decorator to a class level method, turning the attribute into a scalar-like property that can be invoked from the uninstantiated class. The Declarative mapping process looks for these declared_attr callables as it scans classes, and assumes any attribute marked with declared_attr will be a callable that will produce an object specific to the Declarative mapping or table configuration.

declared_attr is usually applicable to mixins, to define relationships that are to be applied to different implementors of the class. It may also be used to define dynamically generated column expressions and other Declarative attributes.

Example:

class ProvidesUserMixin:
    "A mixin that adds a 'user' relationship to classes."

    user_id: Mapped[int] = mapped_column(ForeignKey("user_table.id"))

    @declared_attr
    def user(cls) -> Mapped["User"]:
        return relationship("User")

When used with Declarative directives such as __tablename__, the declared_attr.directive() modifier may be used which indicates to PEP 484 typing tools that the given method is not dealing with Mapped attributes:

class CreateTableName:
    @declared_attr.directive
    def __tablename__(cls) -> str:
        return cls.__name__.lower()

declared_attr can also be applied directly to mapped classes, to allow for attributes that dynamically configure themselves on subclasses when using mapped inheritance schemes. Below illustrates declared_attr to create a dynamic scheme for generating the Mapper.polymorphic_identity parameter for subclasses:

class Employee(Base):
    __tablename__ = 'employee'

    id: Mapped[int] = mapped_column(primary_key=True)
    type: Mapped[str] = mapped_column(String(50))

    @declared_attr.directive
    def __mapper_args__(cls) -> Dict[str, Any]:
        if cls.__name__ == 'Employee':
            return {
                    "polymorphic_on":cls.type,
                    "polymorphic_identity":"Employee"
            }
        else:
            return {"polymorphic_identity":cls.__name__}

class Engineer(Employee):
    pass

declared_attr supports decorating functions that are explicitly decorated with @classmethod. This is never necessary from a runtime perspective, however may be needed in order to support PEP 484 typing tools that don’t otherwise recognize the decorated function as having class-level behaviors for the cls parameter:

class SomethingMixin:
    x: Mapped[int]
    y: Mapped[int]

    @declared_attr
    @classmethod
    def x_plus_y(cls) -> Mapped[int]:
        return column_property(cls.x + cls.y)

New in version 2.0: - declared_attr can accommodate a function decorated with @classmethod to help with PEP 484 integration where needed.

See also

Composing Mapped Hierarchies with Mixins - Declarative Mixin documentation with background on use patterns for declared_attr.

Members

cascading, directive

Class signature

class sqlalchemy.orm.declared_attr (sqlalchemy.orm.base._MappedAttribute, sqlalchemy.orm.decl_api._declared_attr_common)

attribute sqlalchemy.orm.declared_attr.cascading

Mark a declared_attr as cascading.

This is a special-use modifier which indicates that a column or MapperProperty-based declared attribute should be configured distinctly per mapped subclass, within a mapped-inheritance scenario.

Warning

The declared_attr.cascading modifier has several limitations:

• The flag only applies to the use of declared_attr on declarative mixin classes and __abstract__ classes; it currently has no effect when used on a mapped class directly.

• The flag only applies to normally-named attributes, e.g. not any special underscore attributes such as __tablename__. On these attributes it has no effect.

• The flag currently does not allow further overrides down the class hierarchy; if a subclass tries to override the attribute, a warning is emitted and the overridden attribute is skipped. This is a limitation that it is hoped will be resolved at some point.

Below, both MyClass as well as MySubClass will have a distinct id Column object established:

class HasIdMixin:
    @declared_attr.cascading
    def id(cls):
        if has_inherited_table(cls):
            return Column(ForeignKey("myclass.id"), primary_key=True)
        else:
            return Column(Integer, primary_key=True)


class MyClass(HasIdMixin, Base):
    __tablename__ = "myclass"
    # ...


class MySubClass(MyClass):
    """ """

    # ...

The behavior of the above configuration is that MySubClass will refer to both its own id column as well as that of MyClass underneath the attribute named some_id.

See also

Declarative Inheritance

Using _orm.declared_attr() to generate table-specific inheriting columns

attribute sqlalchemy.orm.declared_attr.directive

Mark a declared_attr as decorating a Declarative directive such as __tablename__ or __mapper_args__.

The purpose of declared_attr.directive is strictly to support PEP 484 typing tools, by allowing the decorated function to have a return type that is not using the Mapped generic class, as would normally be the case when declared_attr is used for columns and mapped properties. At runtime, the declared_attr.directive returns the declared_attr class unmodified.

E.g.:

class CreateTableName:
    @declared_attr.directive
    def __tablename__(cls) -> str:
        return cls.__name__.lower()

New in version 2.0.

See also

Composing Mapped Hierarchies with Mixins

declared_attr

class sqlalchemy.orm.DeclarativeBase

Base class used for declarative class definitions.

The DeclarativeBase allows for the creation of new declarative bases in such a way that is compatible with type checkers:

from sqlalchemy.orm import DeclarativeBase

class Base(DeclarativeBase):
    pass

The above Base class is now usable as the base for new declarative mappings. The superclass makes use of the __init_subclass__() method to set up new classes and metaclasses aren’t used.

When first used, the DeclarativeBase class instantiates a new registry to be used with the base, assuming one was not provided explicitly. The DeclarativeBase class supports class-level attributes which act as parameters for the construction of this registry; such as to indicate a specific MetaData collection as well as a specific value for registry.type_annotation_map:

from typing_extensions import Annotated

from sqlalchemy import BigInteger
from sqlalchemy import MetaData
from sqlalchemy import String
from sqlalchemy.orm import DeclarativeBase

bigint = Annotated[int, "bigint"]
my_metadata = MetaData()

class Base(DeclarativeBase):
    metadata = my_metadata
    type_annotation_map = {
        str: String().with_variant(String(255), "mysql", "mariadb"),
        bigint: BigInteger()
    }

Class-level attributes which may be specified include:

Parameters:

• metadata – optional MetaData collection. If a registry is constructed automatically, this MetaData collection will be used to construct it. Otherwise, the local MetaData collection will supercede that used by an existing registry passed using the DeclarativeBase.registry parameter.

• type_annotation_map – optional type annotation map that will be passed to the registry as registry.type_annotation_map.

• registry – supply a pre-existing registry directly.

New in version 2.0: Added DeclarativeBase, so that declarative base classes may be constructed in such a way that is also recognized by PEP 484 type checkers. As a result, DeclarativeBase and other subclassing-oriented APIs should be seen as superseding previous “class returned by a function” APIs, namely declarative_base() and registry.generate_base(), where the base class returned cannot be recognized by type checkers without using plugins.

__init__ behavior

In a plain Python class, the base-most __init__() method in the class hierarchy is object.__init__(), which accepts no arguments. However, when the DeclarativeBase subclass is first declared, the class is given an __init__() method that links to the registry.constructor constructor function, if no __init__() method is already present; this is the usual declarative constructor that will assign keyword arguments as attributes on the instance, assuming those attributes are established at the class level (i.e. are mapped, or are linked to a descriptor). This constructor is never accessed by a mapped class without being called explicitly via super(), as mapped classes are themselves given an __init__() method directly which calls registry.constructor, so in the default case works independently of what the base-most __init__() method does.

Changed in version 2.0.1: DeclarativeBase has a default constructor that links to registry.constructor by default, so that calls to super().__init__() can access this constructor. Previously, due to an implementation mistake, this default constructor was missing, and calling super().__init__() would invoke object.__init__().

The DeclarativeBase subclass may also declare an explicit __init__() method which will replace the use of the registry.constructor function at this level:

class Base(DeclarativeBase):
    def __init__(self, id=None):
        self.id = id

Mapped classes still will not invoke this constructor implicitly; it remains only accessible by calling super().__init__():

class MyClass(Base):
    def __init__(self, id=None, name=None):
        self.name = name
        super().__init__(id=id)

Note that this is a different behavior from what functions like the legacy declarative_base() would do; the base created by those functions would always install registry.constructor for __init__().

Members

__mapper__, __mapper_args__, __table__, __table_args__, __tablename__, metadata, registry

Class signature

class sqlalchemy.orm.DeclarativeBase (sqlalchemy.inspection.Inspectable)

attribute sqlalchemy.orm.DeclarativeBase.__mapper__: ClassVar[Mapper[Any]]

The Mapper object to which a particular class is mapped.

May also be acquired using inspect(), e.g. inspect(klass).

attribute sqlalchemy.orm.DeclarativeBase.__mapper_args__: Any

Dictionary of arguments which will be passed to the Mapper constructor.

See also

Mapper Configuration Options with Declarative

attribute sqlalchemy.orm.DeclarativeBase.__table__: ClassVar[FromClause]

The FromClause to which a particular subclass is mapped.

This is usually an instance of Table but may also refer to other kinds of FromClause such as Subquery, depending on how the class is mapped.

See also

Accessing Table and Metadata

attribute sqlalchemy.orm.DeclarativeBase.__table_args__: Any

A dictionary or tuple of arguments that will be passed to the Table constructor. See Declarative Table Configuration for background on the specific structure of this collection.

See also

Declarative Table Configuration

attribute sqlalchemy.orm.DeclarativeBase.__tablename__: Any

String name to assign to the generated Table object, if not specified directly via DeclarativeBase.__table__.

See also

Declarative Table with mapped_column()

attribute sqlalchemy.orm.DeclarativeBase.metadata: ClassVar[MetaData]

Refers to the MetaData collection that will be used for new Table objects.

See also

Accessing Table and Metadata

attribute sqlalchemy.orm.DeclarativeBase.registry: ClassVar[registry]

Refers to the registry in use where new Mapper objects will be associated.

class sqlalchemy.orm.DeclarativeBaseNoMeta

Same as DeclarativeBase, but does not use a metaclass to intercept new attributes.

The DeclarativeBaseNoMeta base may be used when use of custom metaclasses is desirable.

New in version 2.0.

Members

__mapper__, __mapper_args__, __table__, __table_args__, __tablename__, metadata, registry

Class signature

class sqlalchemy.orm.DeclarativeBaseNoMeta (sqlalchemy.inspection.Inspectable)

attribute sqlalchemy.orm.DeclarativeBaseNoMeta.__mapper__: ClassVar[Mapper[Any]]

The Mapper object to which a particular class is mapped.

May also be acquired using inspect(), e.g. inspect(klass).

attribute sqlalchemy.orm.DeclarativeBaseNoMeta.__mapper_args__: Any

Dictionary of arguments which will be passed to the Mapper constructor.

See also

Mapper Configuration Options with Declarative

attribute sqlalchemy.orm.DeclarativeBaseNoMeta.__table__: FromClause | None

The FromClause to which a particular subclass is mapped.

This is usually an instance of Table but may also refer to other kinds of FromClause such as Subquery, depending on how the class is mapped.

See also

Accessing Table and Metadata

attribute sqlalchemy.orm.DeclarativeBaseNoMeta.__table_args__: Any

A dictionary or tuple of arguments that will be passed to the Table constructor. See Declarative Table Configuration for background on the specific structure of this collection.

See also

Declarative Table Configuration

attribute sqlalchemy.orm.DeclarativeBaseNoMeta.__tablename__: Any

String name to assign to the generated Table object, if not specified directly via DeclarativeBase.__table__.

See also

Declarative Table with mapped_column()

attribute sqlalchemy.orm.DeclarativeBaseNoMeta.metadata: ClassVar[MetaData]

Refers to the MetaData collection that will be used for new Table objects.

See also

Accessing Table and Metadata

attribute sqlalchemy.orm.DeclarativeBaseNoMeta.registry: ClassVar[registry]

Refers to the registry in use where new Mapper objects will be associated.

function sqlalchemy.orm.has_inherited_table(cls: Type[_O]) → bool

Given a class, return True if any of the classes it inherits from has a mapped table, otherwise return False.

This is used in declarative mixins to build attributes that behave differently for the base class vs. a subclass in an inheritance hierarchy.

See also

Using Mixins and Base Classes with Mapped Inheritance Patterns

function sqlalchemy.orm.synonym_for(name: str, map_column: bool = False) → Callable[[Callable[[...], Any]], Synonym[Any]]

Decorator that produces an synonym() attribute in conjunction with a Python descriptor.

The function being decorated is passed to synonym() as the synonym.descriptor parameter:

class MyClass(Base):
    __tablename__ = 'my_table'

    id = Column(Integer, primary_key=True)
    _job_status = Column("job_status", String(50))

    @synonym_for("job_status")
    @property
    def job_status(self):
        return "Status: %s" % self._job_status

The hybrid properties feature of SQLAlchemy is typically preferred instead of synonyms, which is a more legacy feature.

See also

Synonyms - Overview of synonyms

synonym() - the mapper-level function

Using Descriptors and Hybrids - The Hybrid Attribute extension provides an updated approach to augmenting attribute behavior more flexibly than can be achieved with synonyms.

function sqlalchemy.orm.object_mapper(instance: _T) → Mapper[_T]

Given an object, return the primary Mapper associated with the object instance.

Raises sqlalchemy.orm.exc.UnmappedInstanceError if no mapping is configured.

This function is available via the inspection system as:

inspect(instance).mapper

Using the inspection system will raise sqlalchemy.exc.NoInspectionAvailable if the instance is not part of a mapping.

function sqlalchemy.orm.class_mapper(class_: Type[_O], configure: bool = True) → Mapper[_O]

Given a class, return the primary Mapper associated with the key.

Raises UnmappedClassError if no mapping is configured on the given class, or ArgumentError if a non-class object is passed.

Equivalent functionality is available via the inspect() function as:

inspect(some_mapped_class)

Using the inspection system will raise sqlalchemy.exc.NoInspectionAvailable if the class is not mapped.

function sqlalchemy.orm.configure_mappers() → None

Initialize the inter-mapper relationships of all mappers that have been constructed thus far across all registry collections.

The configure step is used to reconcile and initialize the relationship() linkages between mapped classes, as well as to invoke configuration events such as the MapperEvents.before_configured() and MapperEvents.after_configured(), which may be used by ORM extensions or user-defined extension hooks.

Mapper configuration is normally invoked automatically, the first time mappings from a particular registry are used, as well as whenever mappings are used and additional not-yet-configured mappers have been constructed. The automatic configuration process however is local only to the registry involving the target mapper and any related registry objects which it may depend on; this is equivalent to invoking the registry.configure() method on a particular registry.

By contrast, the configure_mappers() function will invoke the configuration process on all registry objects that exist in memory, and may be useful for scenarios where many individual registry objects that are nonetheless interrelated are in use.

Changed in version 1.4: As of SQLAlchemy 1.4.0b2, this function works on a per-registry basis, locating all registry objects present and invoking the registry.configure() method on each. The registry.configure() method may be preferred to limit the configuration of mappers to those local to a particular registry and/or declarative base class.

Points at which automatic configuration is invoked include when a mapped class is instantiated into an instance, as well as when ORM queries are emitted using Session.query() or Session.execute() with an ORM-enabled statement.

The mapper configure process, whether invoked by configure_mappers() or from registry.configure(), provides several event hooks that can be used to augment the mapper configuration step. These hooks include:

• MapperEvents.before_configured() - called once before configure_mappers() or registry.configure() does any work; this can be used to establish additional options, properties, or related mappings before the operation proceeds.

• MapperEvents.mapper_configured() - called as each individual Mapper is configured within the process; will include all mapper state except for backrefs set up by other mappers that are still to be configured.

• MapperEvents.after_configured() - called once after configure_mappers() or registry.configure() is complete; at this stage, all Mapper objects that fall within the scope of the configuration operation will be fully configured. Note that the calling application may still have other mappings that haven’t been produced yet, such as if they are in modules as yet unimported, and may also have mappings that are still to be configured, if they are in other registry collections not part of the current scope of configuration.

function sqlalchemy.orm.clear_mappers() → None

Remove all mappers from all classes.

Changed in version 1.4: This function now locates all registry objects and calls upon the registry.dispose() method of each.

This function removes all instrumentation from classes and disposes of their associated mappers. Once called, the classes are unmapped and can be later re-mapped with new mappers.

clear_mappers() is not for normal use, as there is literally no valid usage for it outside of very specific testing scenarios. Normally, mappers are permanent structural components of user-defined classes, and are never discarded independently of their class. If a mapped class itself is garbage collected, its mapper is automatically disposed of as well. As such, clear_mappers() is only for usage in test suites that re-use the same classes with different mappings, which is itself an extremely rare use case - the only such use case is in fact SQLAlchemy’s own test suite, and possibly the test suites of other ORM extension libraries which intend to test various combinations of mapper construction upon a fixed set of classes.

function sqlalchemy.orm.util.identity_key(class_: Type[_T] | None = None, ident: Any | Tuple[Any, ...] = None, *, instance: _T | None = None, row: Row[Any] | RowMapping | None = None, identity_token: Any | None = None) → _IdentityKeyType[_T]

Generate “identity key” tuples, as are used as keys in the Session.identity_map dictionary.

This function has several call styles:

• identity_key(class, ident, identity_token=token)

  This form receives a mapped class and a primary key scalar or tuple as an argument.

  E.g.:

  >>> identity_key(MyClass, (1, 2))
  (<class '__main__.MyClass'>, (1, 2), None)

  param class:

  mapped class (must be a positional argument)

  param ident:

  primary key, may be a scalar or tuple argument.

  param identity_token:

  optional identity token

  New in version 1.2: added identity_token

• identity_key(instance=instance)

  This form will produce the identity key for a given instance. The instance need not be persistent, only that its primary key attributes are populated (else the key will contain None for those missing values).

  E.g.:

  >>> instance = MyClass(1, 2)
  >>> identity_key(instance=instance)
  (<class '__main__.MyClass'>, (1, 2), None)

  In this form, the given instance is ultimately run though Mapper.identity_key_from_instance(), which will have the effect of performing a database check for the corresponding row if the object is expired.

  param instance:

  object instance (must be given as a keyword arg)

• identity_key(class, row=row, identity_token=token)

  This form is similar to the class/tuple form, except is passed a database result row as a Row or RowMapping object.

  E.g.:

  >>> row = engine.execute(\
      text("select * from table where a=1 and b=2")\
      ).first()
  >>> identity_key(MyClass, row=row)
  (<class '__main__.MyClass'>, (1, 2), None)

  param class:

  mapped class (must be a positional argument)

  param row:

  Row row returned by a CursorResult (must be given as a keyword arg)

  param identity_token:

  optional identity token

  New in version 1.2: added identity_token

function sqlalchemy.orm.polymorphic_union(table_map, typecolname, aliasname='p_union', cast_nulls=True)

Create a UNION statement used by a polymorphic mapper.

See Concrete Table Inheritance for an example of how this is used.

Parameters:

• table_map – mapping of polymorphic identities to Table objects.

• typecolname – string name of a “discriminator” column, which will be derived from the query, producing the polymorphic identity for each row. If None, no polymorphic discriminator is generated.

• aliasname – name of the alias() construct generated.

• cast_nulls – if True, non-existent columns, which are represented as labeled NULLs, will be passed into CAST. This is a legacy behavior that is problematic on some backends such as Oracle - in which case it can be set to False.

function sqlalchemy.orm.orm_insert_sentinel(name: str | None = None, type_: _TypeEngineArgument[Any] | None = None, *, default: Any | None = None, omit_from_statements: bool = True) → MappedColumn[Any]

Provides a surrogate mapped_column() that generates a so-called sentinel column, allowing efficient bulk inserts with deterministic RETURNING sorting for tables that don’t otherwise have qualifying primary key configurations.

Use of orm_insert_sentinel() is analogous to the use of the insert_sentinel() construct within a Core Table construct.

Guidelines for adding this construct to a Declarative mapped class are the same as that of the insert_sentinel() construct; the database table itself also needs to have a column with this name present.

For background on how this object is used, see the section Configuring Sentinel Columns as part of the section “Insert Many Values” Behavior for INSERT statements.

See also

insert_sentinel()

“Insert Many Values” Behavior for INSERT statements

Configuring Sentinel Columns

New in version 2.0.10.

function sqlalchemy.orm.reconstructor(fn)

Decorate a method as the ‘reconstructor’ hook.

Designates a single method as the “reconstructor”, an __init__-like method that will be called by the ORM after the instance has been loaded from the database or otherwise reconstituted.

Tip

The reconstructor() decorator makes use of the InstanceEvents.load() event hook, which can be used directly.

The reconstructor will be invoked with no arguments. Scalar (non-collection) database-mapped attributes of the instance will be available for use within the function. Eagerly-loaded collections are generally not yet available and will usually only contain the first element. ORM state changes made to objects at this stage will not be recorded for the next flush() operation, so the activity within a reconstructor should be conservative.

See also

InstanceEvents.load()

class sqlalchemy.orm.Mapper

Defines an association between a Python class and a database table or other relational structure, so that ORM operations against the class may proceed.

The Mapper object is instantiated using mapping methods present on the registry object. For information about instantiating new Mapper objects, see ORM Mapped Class Overview.

Members

__init__(), add_properties(), add_property(), all_orm_descriptors, attrs, base_mapper, c, cascade_iterator(), class_, class_manager, column_attrs, columns, common_parent(), composites, concrete, configured, entity, get_property(), get_property_by_column(), identity_key_from_instance(), identity_key_from_primary_key(), identity_key_from_row(), inherits, is_mapper, is_sibling(), isa(), iterate_properties, local_table, mapped_table, mapper, non_primary, persist_selectable, polymorphic_identity, polymorphic_iterator(), polymorphic_map, polymorphic_on, primary_key, primary_key_from_instance(), primary_mapper(), relationships, selectable, self_and_descendants, single, synonyms, tables, validators, with_polymorphic_mappers

Class signature

class sqlalchemy.orm.Mapper (sqlalchemy.orm.ORMFromClauseRole, sqlalchemy.orm.ORMEntityColumnsClauseRole, sqlalchemy.sql.cache_key.MemoizedHasCacheKey, sqlalchemy.orm.base.InspectionAttr, sqlalchemy.log.Identified, sqlalchemy.inspection.Inspectable, sqlalchemy.event.registry.EventTarget, typing.Generic)

method sqlalchemy.orm.Mapper.__init__(class_: Type[_O], local_table: FromClause | None = None, properties: Mapping[str, MapperProperty[Any]] | None = None, primary_key: Iterable[_ORMColumnExprArgument[Any]] | None = None, non_primary: bool = False, inherits: Mapper[Any] | Type[Any] | None = None, inherit_condition: _ColumnExpressionArgument[bool] | None = None, inherit_foreign_keys: Sequence[_ORMColumnExprArgument[Any]] | None = None, always_refresh: bool = False, version_id_col: _ORMColumnExprArgument[Any] | None = None, version_id_generator: Literal[False] | Callable[[Any], Any] | None = None, polymorphic_on: _ORMColumnExprArgument[Any] | str | MapperProperty[Any] | None = None, _polymorphic_map: Dict[Any, Mapper[Any]] | None = None, polymorphic_identity: Any | None = None, concrete: bool = False, with_polymorphic: _WithPolymorphicArg | None = None, polymorphic_abstract: bool = False, polymorphic_load: Literal['selectin', 'inline'] | None = None, allow_partial_pks: bool = True, batch: bool = True, column_prefix: str | None = None, include_properties: Sequence[str] | None = None, exclude_properties: Sequence[str] | None = None, passive_updates: bool = True, passive_deletes: bool = False, confirm_deleted_rows: bool = True, eager_defaults: Literal[True, False, 'auto'] = 'auto', legacy_is_orphan: bool = False, _compiled_cache_size: int = 100)

Direct constructor for a new Mapper object.

The Mapper constructor is not called directly, and is normally invoked through the use of the registry object through either the Declarative or Imperative mapping styles.

Changed in version 2.0: The public facing mapper() function is removed; for a classical mapping configuration, use the registry.map_imperatively() method.

Parameters documented below may be passed to either the registry.map_imperatively() method, or may be passed in the __mapper_args__ declarative class attribute described at Mapper Configuration Options with Declarative.

Parameters:

• class_ – The class to be mapped. When using Declarative, this argument is automatically passed as the declared class itself.

• local_table – The Table or other FromClause (i.e. selectable) to which the class is mapped. May be None if this mapper inherits from another mapper using single-table inheritance. When using Declarative, this argument is automatically passed by the extension, based on what is configured via the DeclarativeBase.__table__ attribute or via the Table produced as a result of the DeclarativeBase.__tablename__ attribute being present.

• polymorphic_abstract –

  Indicates this class will be mapped in a polymorphic hierarchy, but not directly instantiated. The class is mapped normally, except that it has no requirement for a Mapper.polymorphic_identity within an inheritance hierarchy. The class however must be part of a polymorphic inheritance scheme which uses Mapper.polymorphic_on at the base.

  New in version 2.0.

  See also

  Building Deeper Hierarchies with polymorphic_abstract

• always_refresh – If True, all query operations for this mapped class will overwrite all data within object instances that already exist within the session, erasing any in-memory changes with whatever information was loaded from the database. Usage of this flag is highly discouraged; as an alternative, see the method Query.populate_existing().

• allow_partial_pks – Defaults to True. Indicates that a composite primary key with some NULL values should be considered as possibly existing within the database. This affects whether a mapper will assign an incoming row to an existing identity, as well as if Session.merge() will check the database first for a particular primary key value. A “partial primary key” can occur if one has mapped to an OUTER JOIN, for example.

• batch – Defaults to True, indicating that save operations of multiple entities can be batched together for efficiency. Setting to False indicates that an instance will be fully saved before saving the next instance. This is used in the extremely rare case that a MapperEvents listener requires being called in between individual row persistence operations.

• column_prefix –

  A string which will be prepended to the mapped attribute name when Column objects are automatically assigned as attributes to the mapped class. Does not affect Column objects that are mapped explicitly in the Mapper.properties dictionary.

  This parameter is typically useful with imperative mappings that keep the Table object separate. Below, assuming the user_table Table object has columns named user_id, user_name, and password:

  class User(Base):
      __table__ = user_table
      __mapper_args__ = {'column_prefix':'_'}

  The above mapping will assign the user_id, user_name, and password columns to attributes named _user_id, _user_name, and _password on the mapped User class.

  The Mapper.column_prefix parameter is uncommon in modern use. For dealing with reflected tables, a more flexible approach to automating a naming scheme is to intercept the Column objects as they are reflected; see the section Automating Column Naming Schemes from Reflected Tables for notes on this usage pattern.

• concrete –

  If True, indicates this mapper should use concrete table inheritance with its parent mapper.

  See the section Concrete Table Inheritance for an example.

• confirm_deleted_rows – defaults to True; when a DELETE occurs of one more rows based on specific primary keys, a warning is emitted when the number of rows matched does not equal the number of rows expected. This parameter may be set to False to handle the case where database ON DELETE CASCADE rules may be deleting some of those rows automatically. The warning may be changed to an exception in a future release.

• eager_defaults –

  if True, the ORM will immediately fetch the value of server-generated default values after an INSERT or UPDATE, rather than leaving them as expired to be fetched on next access. This can be used for event schemes where the server-generated values are needed immediately before the flush completes.

  The fetch of values occurs either by using RETURNING inline with the INSERT or UPDATE statement, or by adding an additional SELECT statement subsequent to the INSERT or UPDATE, if the backend does not support RETURNING.

  The use of RETURNING is extremely performant in particular for INSERT statements where SQLAlchemy can take advantage of insertmanyvalues, whereas the use of an additional SELECT is relatively poor performing, adding additional SQL round trips which would be unnecessary if these new attributes are not to be accessed in any case.

  For this reason, Mapper.eager_defaults defaults to the string value "auto", which indicates that server defaults for INSERT should be fetched using RETURNING if the backing database supports it and if the dialect in use supports “insertmanyreturning” for an INSERT statement. If the backing database does not support RETURNING or “insertmanyreturning” is not available, server defaults will not be fetched.

  Changed in version 2.0.0rc1: added the “auto” option for Mapper.eager_defaults

  See also

  Fetching Server-Generated Defaults

  Changed in version 2.0.0: RETURNING now works with multiple rows INSERTed at once using the insertmanyvalues feature, which among other things allows the Mapper.eager_defaults feature to be very performant on supporting backends.

• exclude_properties –

  A list or set of string column names to be excluded from mapping.

  See also

  Mapping a Subset of Table Columns

• include_properties –

  An inclusive list or set of string column names to map.

  See also

  Mapping a Subset of Table Columns

• inherits –

  A mapped class or the corresponding Mapper of one indicating a superclass to which this Mapper should inherit from. The mapped class here must be a subclass of the other mapper’s class. When using Declarative, this argument is passed automatically as a result of the natural class hierarchy of the declared classes.

  See also

  Mapping Class Inheritance Hierarchies

• inherit_condition – For joined table inheritance, a SQL expression which will define how the two tables are joined; defaults to a natural join between the two tables.

• inherit_foreign_keys – When inherit_condition is used and the columns present are missing a ForeignKey configuration, this parameter can be used to specify which columns are “foreign”. In most cases can be left as None.

• legacy_is_orphan –

  Boolean, defaults to False. When True, specifies that “legacy” orphan consideration is to be applied to objects mapped by this mapper, which means that a pending (that is, not persistent) object is auto-expunged from an owning Session only when it is de-associated from all parents that specify a delete-orphan cascade towards this mapper. The new default behavior is that the object is auto-expunged when it is de-associated with any of its parents that specify delete-orphan cascade. This behavior is more consistent with that of a persistent object, and allows behavior to be consistent in more scenarios independently of whether or not an orphan object has been flushed yet or not.

  See the change note and example at The consideration of a “pending” object as an “orphan” has been made more aggressive for more detail on this change.

• non_primary –

  Specify that this Mapper

  is in addition to the “primary” mapper, that is, the one used for persistence. The Mapper created here may be used for ad-hoc mapping of the class to an alternate selectable, for loading only.

  Deprecated since version 1.3: The mapper.non_primary parameter is deprecated, and will be removed in a future release. The functionality of non primary mappers is now better suited using the AliasedClass construct, which can also be used as the target of a relationship() in 1.3.

  See also

  Relationship to Aliased Class - the new pattern that removes the need for the Mapper.non_primary flag.

• passive_deletes –

  Indicates DELETE behavior of foreign key columns when a joined-table inheritance entity is being deleted. Defaults to False for a base mapper; for an inheriting mapper, defaults to False unless the value is set to True on the superclass mapper.

  When True, it is assumed that ON DELETE CASCADE is configured on the foreign key relationships that link this mapper’s table to its superclass table, so that when the unit of work attempts to delete the entity, it need only emit a DELETE statement for the superclass table, and not this table.

  When False, a DELETE statement is emitted for this mapper’s table individually. If the primary key attributes local to this table are unloaded, then a SELECT must be emitted in order to validate these attributes; note that the primary key columns of a joined-table subclass are not part of the “primary key” of the object as a whole.

  Note that a value of True is always forced onto the subclass mappers; that is, it’s not possible for a superclass to specify passive_deletes without this taking effect for all subclass mappers.

  See also

  Using foreign key ON DELETE cascade with ORM relationships - description of similar feature as used with relationship()

  mapper.passive_updates - supporting ON UPDATE CASCADE for joined-table inheritance mappers

• passive_updates –

  Indicates UPDATE behavior of foreign key columns when a primary key column changes on a joined-table inheritance mapping. Defaults to True.

  When True, it is assumed that ON UPDATE CASCADE is configured on the foreign key in the database, and that the database will handle propagation of an UPDATE from a source column to dependent columns on joined-table rows.

  When False, it is assumed that the database does not enforce referential integrity and will not be issuing its own CASCADE operation for an update. The unit of work process will emit an UPDATE statement for the dependent columns during a primary key change.

  See also

  Mutable Primary Keys / Update Cascades - description of a similar feature as used with relationship()

  mapper.passive_deletes - supporting ON DELETE CASCADE for joined-table inheritance mappers

• polymorphic_load –

  Specifies “polymorphic loading” behavior for a subclass in an inheritance hierarchy (joined and single table inheritance only). Valid values are:

  • “‘inline’” - specifies this class should be part of the “with_polymorphic” mappers, e.g. its columns will be included in a SELECT query against the base.

  • “‘selectin’” - specifies that when instances of this class are loaded, an additional SELECT will be emitted to retrieve the columns specific to this subclass. The SELECT uses IN to fetch multiple subclasses at once.

  New in version 1.2.

  See also

  Configuring with_polymorphic() on mappers

  Using selectin_polymorphic()

• polymorphic_on –

  Specifies the column, attribute, or SQL expression used to determine the target class for an incoming row, when inheriting classes are present.

  May be specified as a string attribute name, or as a SQL expression such as a Column or in a Declarative mapping a mapped_column() object. It is typically expected that the SQL expression corresponds to a column in the base-most mapped Table:

  class Employee(Base):
      __tablename__ = 'employee'
  
      id: Mapped[int] = mapped_column(primary_key=True)
      discriminator: Mapped[str] = mapped_column(String(50))
  
      __mapper_args__ = {
          "polymorphic_on":discriminator,
          "polymorphic_identity":"employee"
      }

  It may also be specified as a SQL expression, as in this example where we use the case() construct to provide a conditional approach:

  class Employee(Base):
      __tablename__ = 'employee'
  
      id: Mapped[int] = mapped_column(primary_key=True)
      discriminator: Mapped[str] = mapped_column(String(50))
  
      __mapper_args__ = {
          "polymorphic_on":case(
              (discriminator == "EN", "engineer"),
              (discriminator == "MA", "manager"),
              else_="employee"),
          "polymorphic_identity":"employee"
      }

  It may also refer to any attribute using its string name, which is of particular use when using annotated column configurations:

  class Employee(Base):
      __tablename__ = 'employee'
  
      id: Mapped[int] = mapped_column(primary_key=True)
      discriminator: Mapped[str]
  
      __mapper_args__ = {
          "polymorphic_on": "discriminator",
          "polymorphic_identity": "employee"
      }

  When setting polymorphic_on to reference an attribute or expression that’s not present in the locally mapped Table, yet the value of the discriminator should be persisted to the database, the value of the discriminator is not automatically set on new instances; this must be handled by the user, either through manual means or via event listeners. A typical approach to establishing such a listener looks like:

  from sqlalchemy import event
  from sqlalchemy.orm import object_mapper
  
  @event.listens_for(Employee, "init", propagate=True)
  def set_identity(instance, *arg, **kw):
      mapper = object_mapper(instance)
      instance.discriminator = mapper.polymorphic_identity

  Where above, we assign the value of polymorphic_identity for the mapped class to the discriminator attribute, thus persisting the value to the discriminator column in the database.

  Warning

  Currently, only one discriminator column may be set, typically on the base-most class in the hierarchy. “Cascading” polymorphic columns are not yet supported.

  See also

  Mapping Class Inheritance Hierarchies

• polymorphic_identity –

  Specifies the value which identifies this particular class as returned by the column expression referred to by the Mapper.polymorphic_on setting. As rows are received, the value corresponding to the Mapper.polymorphic_on column expression is compared to this value, indicating which subclass should be used for the newly reconstructed object.

  See also

  Mapping Class Inheritance Hierarchies

• properties –

  A dictionary mapping the string names of object attributes to MapperProperty instances, which define the persistence behavior of that attribute. Note that Column objects present in the mapped Table are automatically placed into ColumnProperty instances upon mapping, unless overridden. When using Declarative, this argument is passed automatically, based on all those MapperProperty instances declared in the declared class body.

  See also

  The properties dictionary - in the ORM Mapped Class Overview

• primary_key –

  A list of Column objects, or alternatively string names of attribute names which refer to Column, which define the primary key to be used against this mapper’s selectable unit. This is normally simply the primary key of the local_table, but can be overridden here.

  Changed in version 2.0.2: Mapper.primary_key arguments may be indicated as string attribute names as well.

  See also

  Mapping to an Explicit Set of Primary Key Columns - background and example use

• version_id_col –

  A Column that will be used to keep a running version id of rows in the table. This is used to detect concurrent updates or the presence of stale data in a flush. The methodology is to detect if an UPDATE statement does not match the last known version id, a StaleDataError exception is thrown. By default, the column must be of Integer type, unless version_id_generator specifies an alternative version generator.

  See also

  Configuring a Version Counter - discussion of version counting and rationale.

• version_id_generator –

  Define how new version ids should be generated. Defaults to None, which indicates that a simple integer counting scheme be employed. To provide a custom versioning scheme, provide a callable function of the form:

  def generate_version(version):
      return next_version

  Alternatively, server-side versioning functions such as triggers, or programmatic versioning schemes outside of the version id generator may be used, by specifying the value False. Please see Server Side Version Counters for a discussion of important points when using this option.

  See also

  Custom Version Counters / Types

  Server Side Version Counters

• with_polymorphic –

  A tuple in the form (<classes>, <selectable>) indicating the default style of “polymorphic” loading, that is, which tables are queried at once. <classes> is any single or list of mappers and/or classes indicating the inherited classes that should be loaded at once. The special value '*' may be used to indicate all descending classes should be loaded immediately. The second tuple argument <selectable> indicates a selectable that will be used to query for multiple classes.

  The Mapper.polymorphic_load parameter may be preferable over the use of Mapper.with_polymorphic in modern mappings to indicate a per-subclass technique of indicating polymorphic loading styles.

  See also

  Configuring with_polymorphic() on mappers

method sqlalchemy.orm.Mapper.add_properties(dict_of_properties)

Add the given dictionary of properties to this mapper, using add_property.

method sqlalchemy.orm.Mapper.add_property(key: str, prop: Column[Any] | MapperProperty[Any]) → None

Add an individual MapperProperty to this mapper.

If the mapper has not been configured yet, just adds the property to the initial properties dictionary sent to the constructor. If this Mapper has already been configured, then the given MapperProperty is configured immediately.

attribute sqlalchemy.orm.Mapper.all_orm_descriptors

A namespace of all InspectionAttr attributes associated with the mapped class.

These attributes are in all cases Python descriptors associated with the mapped class or its superclasses.

This namespace includes attributes that are mapped to the class as well as attributes declared by extension modules. It includes any Python descriptor type that inherits from InspectionAttr. This includes QueryableAttribute, as well as extension types such as hybrid_property, hybrid_method and AssociationProxy.

To distinguish between mapped attributes and extension attributes, the attribute InspectionAttr.extension_type will refer to a constant that distinguishes between different extension types.

The sorting of the attributes is based on the following rules:

1. Iterate through the class and its superclasses in order from subclass to superclass (i.e. iterate through cls.__mro__)

2. For each class, yield the attributes in the order in which they appear in __dict__, with the exception of those in step 3 below. In Python 3.6 and above this ordering will be the same as that of the class’ construction, with the exception of attributes that were added after the fact by the application or the mapper.

3. If a certain attribute key is also in the superclass __dict__, then it’s included in the iteration for that class, and not the class in which it first appeared.

The above process produces an ordering that is deterministic in terms of the order in which attributes were assigned to the class.

Changed in version 1.3.19: ensured deterministic ordering for Mapper.all_orm_descriptors().

When dealing with a QueryableAttribute, the QueryableAttribute.property attribute refers to the MapperProperty property, which is what you get when referring to the collection of mapped properties via Mapper.attrs.

Warning

The Mapper.all_orm_descriptors accessor namespace is an instance of OrderedProperties. This is a dictionary-like object which includes a small number of named methods such as OrderedProperties.items() and OrderedProperties.values(). When accessing attributes dynamically, favor using the dict-access scheme, e.g. mapper.all_orm_descriptors[somename] over getattr(mapper.all_orm_descriptors, somename) to avoid name collisions.

See also

Mapper.attrs

attribute sqlalchemy.orm.Mapper.attrs

A namespace of all MapperProperty objects associated this mapper.

This is an object that provides each property based on its key name. For instance, the mapper for a User class which has User.name attribute would provide mapper.attrs.name, which would be the ColumnProperty representing the name column. The namespace object can also be iterated, which would yield each MapperProperty.

Mapper has several pre-filtered views of this attribute which limit the types of properties returned, including synonyms, column_attrs, relationships, and composites.

Warning

The Mapper.attrs accessor namespace is an instance of OrderedProperties. This is a dictionary-like object which includes a small number of named methods such as OrderedProperties.items() and OrderedProperties.values(). When accessing attributes dynamically, favor using the dict-access scheme, e.g. mapper.attrs[somename] over getattr(mapper.attrs, somename) to avoid name collisions.

See also

Mapper.all_orm_descriptors

attribute sqlalchemy.orm.Mapper.base_mapper: Mapper[Any]

The base-most Mapper in an inheritance chain.

In a non-inheriting scenario, this attribute will always be this Mapper. In an inheritance scenario, it references the Mapper which is parent to all other Mapper objects in the inheritance chain.

This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

attribute sqlalchemy.orm.Mapper.c: ReadOnlyColumnCollection[str, Column[Any]]

A synonym for Mapper.columns.

method sqlalchemy.orm.Mapper.cascade_iterator(type_: str, state: InstanceState[_O], halt_on: Callable[[InstanceState[Any]], bool] | None = None) → Iterator[Tuple[object, Mapper[Any], InstanceState[Any], _InstanceDict]]

Iterate each element and its mapper in an object graph, for all relationships that meet the given cascade rule.

Parameters:

• type_ –

  The name of the cascade rule (i.e. "save-update", "delete", etc.).

  Note

  the "all" cascade is not accepted here. For a generic object traversal function, see How do I walk all objects that are related to a given object?.

• state – The lead InstanceState. child items will be processed per the relationships defined for this object’s mapper.

Returns:

the method yields individual object instances.

See also

Cascades

How do I walk all objects that are related to a given object? - illustrates a generic function to traverse all objects without relying on cascades.

attribute sqlalchemy.orm.Mapper.class_: Type[_O]

The class to which this Mapper is mapped.

attribute sqlalchemy.orm.Mapper.class_manager: ClassManager[_O]

The ClassManager which maintains event listeners and class-bound descriptors for this Mapper.

This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

attribute sqlalchemy.orm.Mapper.column_attrs

Return a namespace of all ColumnProperty properties maintained by this Mapper.

See also

Mapper.attrs - namespace of all MapperProperty objects.

attribute sqlalchemy.orm.Mapper.columns: ReadOnlyColumnCollection[str, Column[Any]]

A collection of Column or other scalar expression objects maintained by this Mapper.

The collection behaves the same as that of the c attribute on any Table object, except that only those columns included in this mapping are present, and are keyed based on the attribute name defined in the mapping, not necessarily the key attribute of the Column itself. Additionally, scalar expressions mapped by column_property() are also present here.

This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

method sqlalchemy.orm.Mapper.common_parent(other: Mapper[Any]) → bool

Return true if the given mapper shares a common inherited parent as this mapper.

attribute sqlalchemy.orm.Mapper.composites

Return a namespace of all Composite properties maintained by this Mapper.

See also

Mapper.attrs - namespace of all MapperProperty objects.

attribute sqlalchemy.orm.Mapper.concrete: bool

Represent True if this Mapper is a concrete inheritance mapper.

This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

attribute sqlalchemy.orm.Mapper.configured: bool = False

Represent True if this Mapper has been configured.

This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

See also

configure_mappers().

attribute sqlalchemy.orm.Mapper.entity

Part of the inspection API.

Returns self.class_.

method sqlalchemy.orm.Mapper.get_property(key: str, _configure_mappers: bool = False) → MapperProperty[Any]

return a MapperProperty associated with the given key.

method sqlalchemy.orm.Mapper.get_property_by_column(column: ColumnElement[_T]) → MapperProperty[_T]

Given a Column object, return the MapperProperty which maps this column.

method sqlalchemy.orm.Mapper.identity_key_from_instance(instance: _O) → _IdentityKeyType[_O]

Return the identity key for the given instance, based on its primary key attributes.

If the instance’s state is expired, calling this method will result in a database check to see if the object has been deleted. If the row no longer exists, ObjectDeletedError is raised.

This value is typically also found on the instance state under the attribute name key.

method sqlalchemy.orm.Mapper.identity_key_from_primary_key(primary_key: Tuple[Any, ...], identity_token: Any | None = None) → _IdentityKeyType[_O]

Return an identity-map key for use in storing/retrieving an item from an identity map.

Parameters:

primary_key – A list of values indicating the identifier.

method sqlalchemy.orm.Mapper.identity_key_from_row(row: Row[Any] | RowMapping | None, identity_token: Any | None = None, adapter: ORMAdapter | None = None) → _IdentityKeyType[_O]

Return an identity-map key for use in storing/retrieving an item from the identity map.

Parameters:

row –

A Row or RowMapping produced from a result set that selected from the ORM mapped primary key columns.

Changed in version 2.0: Row or RowMapping are accepted for the “row” argument

attribute sqlalchemy.orm.Mapper.inherits: Mapper[Any] | None

References the Mapper which this Mapper inherits from, if any.

attribute sqlalchemy.orm.Mapper.is_mapper = True

Part of the inspection API.

method sqlalchemy.orm.Mapper.is_sibling(other: Mapper[Any]) → bool

return true if the other mapper is an inheriting sibling to this one. common parent but different branch

method sqlalchemy.orm.Mapper.isa(other: Mapper[Any]) → bool

Return True if the this mapper inherits from the given mapper.

attribute sqlalchemy.orm.Mapper.iterate_properties

return an iterator of all MapperProperty objects.

attribute sqlalchemy.orm.Mapper.local_table: FromClause

The immediate FromClause to which this Mapper refers.

Typically is an instance of Table, may be any FromClause.

The “local” table is the selectable that the Mapper is directly responsible for managing from an attribute access and flush perspective. For non-inheriting mappers, Mapper.local_table will be the same as Mapper.persist_selectable. For inheriting mappers, Mapper.local_table refers to the specific portion of Mapper.persist_selectable that includes the columns to which this Mapper is loading/persisting, such as a particular Table within a join.

See also

Mapper.persist_selectable.

Mapper.selectable.

attribute sqlalchemy.orm.Mapper.mapped_table

Deprecated since version 1.3: Use .persist_selectable

attribute sqlalchemy.orm.Mapper.mapper

Part of the inspection API.

Returns self.

attribute sqlalchemy.orm.Mapper.non_primary: bool

Represent True if this Mapper is a “non-primary” mapper, e.g. a mapper that is used only to select rows but not for persistence management.

This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

attribute sqlalchemy.orm.Mapper.persist_selectable: FromClause

The FromClause to which this Mapper is mapped.

Typically is an instance of Table, may be any FromClause.

The Mapper.persist_selectable is similar to Mapper.local_table, but represents the FromClause that represents the inheriting class hierarchy overall in an inheritance scenario.

:attr.`.Mapper.persist_selectable` is also separate from the Mapper.selectable attribute, the latter of which may be an alternate subquery used for selecting columns. :attr.`.Mapper.persist_selectable` is oriented towards columns that will be written on a persist operation.

See also

Mapper.selectable.

Mapper.local_table.

attribute sqlalchemy.orm.Mapper.polymorphic_identity: Any | None

Represent an identifier which is matched against the Mapper.polymorphic_on column during result row loading.

Used only with inheritance, this object can be of any type which is comparable to the type of column represented by Mapper.polymorphic_on.

This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

method sqlalchemy.orm.Mapper.polymorphic_iterator() → Iterator[Mapper[Any]]

Iterate through the collection including this mapper and all descendant mappers.

This includes not just the immediately inheriting mappers but all their inheriting mappers as well.

To iterate through an entire hierarchy, use mapper.base_mapper.polymorphic_iterator().

attribute sqlalchemy.orm.Mapper.polymorphic_map: Dict[Any, Mapper[Any]]

A mapping of “polymorphic identity” identifiers mapped to Mapper instances, within an inheritance scenario.

The identifiers can be of any type which is comparable to the type of column represented by Mapper.polymorphic_on.

An inheritance chain of mappers will all reference the same polymorphic map object. The object is used to correlate incoming result rows to target mappers.

This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

attribute sqlalchemy.orm.Mapper.polymorphic_on: KeyedColumnElement[Any] | None

The Column or SQL expression specified as the polymorphic_on argument for this Mapper, within an inheritance scenario.

This attribute is normally a Column instance but may also be an expression, such as one derived from cast().

This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

attribute sqlalchemy.orm.Mapper.primary_key: Tuple[Column[Any], ...]

An iterable containing the collection of Column objects which comprise the ‘primary key’ of the mapped table, from the perspective of this Mapper.

This list is against the selectable in Mapper.persist_selectable. In the case of inheriting mappers, some columns may be managed by a superclass mapper. For example, in the case of a Join, the primary key is determined by all of the primary key columns across all tables referenced by the Join.

The list is also not necessarily the same as the primary key column collection associated with the underlying tables; the Mapper features a primary_key argument that can override what the Mapper considers as primary key columns.

This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

method sqlalchemy.orm.Mapper.primary_key_from_instance(instance: _O) → Tuple[Any, ...]

Return the list of primary key values for the given instance.

If the instance’s state is expired, calling this method will result in a database check to see if the object has been deleted. If the row no longer exists, ObjectDeletedError is raised.

method sqlalchemy.orm.Mapper.primary_mapper() → Mapper[Any]

Return the primary mapper corresponding to this mapper’s class key (class).

attribute sqlalchemy.orm.Mapper.relationships

A namespace of all Relationship properties maintained by this Mapper.

Warning

the Mapper.relationships accessor namespace is an instance of OrderedProperties. This is a dictionary-like object which includes a small number of named methods such as OrderedProperties.items() and OrderedProperties.values(). When accessing attributes dynamically, favor using the dict-access scheme, e.g. mapper.relationships[somename] over getattr(mapper.relationships, somename) to avoid name collisions.

See also

Mapper.attrs - namespace of all MapperProperty objects.

attribute sqlalchemy.orm.Mapper.selectable

The FromClause construct this Mapper selects from by default.

Normally, this is equivalent to persist_selectable, unless the with_polymorphic feature is in use, in which case the full “polymorphic” selectable is returned.

attribute sqlalchemy.orm.Mapper.self_and_descendants

The collection including this mapper and all descendant mappers.

This includes not just the immediately inheriting mappers but all their inheriting mappers as well.

attribute sqlalchemy.orm.Mapper.single: bool

Represent True if this Mapper is a single table inheritance mapper.

Mapper.local_table will be None if this flag is set.

This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

attribute sqlalchemy.orm.Mapper.synonyms

Return a namespace of all Synonym properties maintained by this Mapper.

See also

Mapper.attrs - namespace of all MapperProperty objects.

attribute sqlalchemy.orm.Mapper.tables: Sequence[TableClause]

A sequence containing the collection of Table or TableClause objects which this Mapper is aware of.

If the mapper is mapped to a Join, or an Alias representing a Select, the individual Table objects that comprise the full construct will be represented here.

This is a read only attribute determined during mapper construction. Behavior is undefined if directly modified.

attribute sqlalchemy.orm.Mapper.validators: util.immutabledict[str, Tuple[str, Dict[str, Any]]]

An immutable dictionary of attributes which have been decorated using the validates() decorator.

The dictionary contains string attribute names as keys mapped to the actual validation method.

attribute sqlalchemy.orm.Mapper.with_polymorphic_mappers

The list of Mapper objects included in the default “polymorphic” query.

class sqlalchemy.orm.MappedAsDataclass

Mixin class to indicate when mapping this class, also convert it to be a dataclass.

See also

Declarative Dataclass Mapping - complete background on SQLAlchemy native dataclass mapping

New in version 2.0.

class sqlalchemy.orm.MappedClassProtocol

A protocol representing a SQLAlchemy mapped class.

The protocol is generic on the type of class, use MappedClassProtocol[Any] to allow any mapped class.

Class signature

class sqlalchemy.orm.MappedClassProtocol (typing_extensions.Protocol)