Release: 2.0.51 | Release Date: June 15, 2026

# SQLAlchemy 2.0 Documentation

# 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, hash, active_history, expire_on_flush, info, doc, dataclass_metadata]) | 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_as_dataclass(registry, *, [init, repr, eq, order, unsafe_hash, match_args, kw_only, dataclass_callable]) | Standalone function form of `registry.mapped_as_dataclass()` which may have better compatibility with mypy.

mapped_column([__name_pos, __type_pos], *args, [init, repr, default, default_factory, compare, kw_only, hash, 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, dataclass_metadata], **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.

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.

  Added in version 2.0.

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.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.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 reuse 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.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, hash: _NoArg | bool | None = _NoArg.NO_ARG, active_history: bool = False, expire_on_flush: bool = True, info: _InfoType | None = None, doc: str | None = None, dataclass_metadata: _NoArg | Mapping[Any, Any] | None = _NoArg.NO_ARG) → 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.

      Added in version 1.4.

      See also

      Using raiseload to prevent deferred column loads

    - 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.

      See also

      What are default, default_factory and insert_default and what should I use?

      `mapped_column.default`

      `mapped_column.insert_default`

    - 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.

      Added 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__()`.

    - hash –

      Specific to Declarative Dataclass Mapping, controls if this field is included when generating the `__hash__()` method for the mapped class.

      Added in version 2.0.36.

    - dataclass_metadata –

      Specific to Declarative Dataclass Mapping, supplies metadata to be attached to the generated dataclass field.

      Added in version 2.0.42.

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.declarative_base(*, metadata: MetaData | None = None, mapper: Optional[Callable[..., Mapper[Any]]]=None, cls: Type[Any] = <class 'object'>, name: str = 'Base', class_registry: clsregistry._ClsRegistryType | None = None, type_annotation_map: _TypeAnnotationMapType | None = 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.

      Added 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.

  Added in version 1.4.6.

  Legacy Feature

  This api is considered legacy and will be deprecated in the next SQLAlchemy version.

  See also

  Composing Mapped Hierarchies with Mixins

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

class sqlalchemy.orm.DeclarativeBase

  inherits from `sqlalchemy.inspection.Inspectable`

  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 supersede 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.

  Added 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__()`.

  Member Name | Description
  --- | ---
  __mapper__ | The `Mapper` object to which a particular class is mapped.
  __mapper_args__ | Dictionary of arguments which will be passed to the `Mapper` constructor.
  __table__ | The `FromClause` to which a particular subclass is mapped.
  __table_args__ | 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.
  __tablename__ | String name to assign to the generated `Table` object, if not specified directly via `DeclarativeBase.__table__`.
  metadata | Refers to the `MetaData` collection that will be used for new `Table` objects.
  registry | Refers to the `registry` in use where new `Mapper` objects will be associated.

  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

  inherits from `sqlalchemy.inspection.Inspectable`

  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.

  Added in version 2.0.

  Member Name | Description
  --- | ---
  __mapper__ | The `Mapper` object to which a particular class is mapped.
  __mapper_args__ | Dictionary of arguments which will be passed to the `Mapper` constructor.
  __table__ | The `FromClause` to which a particular subclass is mapped.
  __table_args__ | 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.
  __tablename__ | String name to assign to the generated `Table` object, if not specified directly via `DeclarativeBase.__table__`.
  metadata | Refers to the `MetaData` collection that will be used for new `Table` objects.
  registry | Refers to the `registry` in use where new `Mapper` objects will be associated.

  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.

class sqlalchemy.orm.declared_attr

  inherits from `sqlalchemy.orm.base._MappedAttribute`, `sqlalchemy.orm.decl_api._declared_attr_common`

  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)

  Added 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`.

  Member Name | Description
  --- | ---
  cascading | Mark a `declared_attr` as cascading.
  directive | Mark a `declared_attr` as decorating a Declarative directive such as `__tablename__` or `__mapper_args__`.

  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) -> Mapped[int]:
                if has_inherited_table(cls):
                    return mapped_column(ForeignKey("myclass.id"), primary_key=True)
                else:
                    return mapped_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()

    Added in version 2.0.

    See also

    Composing Mapped Hierarchies with Mixins

    `declared_attr`

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.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

      Added 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

      Added in version 1.2: added identity_token

function sqlalchemy.orm.mapped_as_dataclass(registry: RegistryType, *, 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) → Callable[[Type[_O]], Type[_O]]

  Standalone function form of `registry.mapped_as_dataclass()` which may have better compatibility with mypy.

  The `registry` is passed as the first argument to the decorator.

  e.g.:

      from sqlalchemy.orm import Mapped
      from sqlalchemy.orm import mapped_as_dataclass
      from sqlalchemy.orm import mapped_column
      from sqlalchemy.orm import registry

      some_registry = registry()

      @mapped_as_dataclass(some_registry)
      class Relationships:
          __tablename__ = "relationships"

          entity_id1: Mapped[int] = mapped_column(primary_key=True)
          entity_id2: Mapped[int] = mapped_column(primary_key=True)
          level: Mapped[int] = mapped_column(Integer)

  Added in version 2.0.44.

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, hash: _NoArg | bool | None = _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: _ServerOnUpdateArgument | None = None, active_history: bool = False, quote: bool | None = None, system: bool = False, comment: str | None = None, sort_order: _NoArg | int = _NoArg.NO_ARG, dataclass_metadata: _NoArg | Mapping[Any, Any] | None = _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.

  Added 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` (or its equivalent) 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

      Added 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.

      See also

      What are default, default_factory and insert_default and what should I use?

      `mapped_column.insert_default`

      `mapped_column.default_factory`

    - 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.

      See also

      What are default, default_factory and insert_default and what should I use?

      `mapped_column.default`

      `mapped_column.default_factory`

    - 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.

      Added 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.

      Added 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.

      See also

      What are default, default_factory and insert_default and what should I use?

      `mapped_column.default`

      `mapped_column.insert_default`

    - 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.

      Added 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__()`.

    - hash –

      Specific to Declarative Dataclass Mapping, controls if this field is included when generating the `__hash__()` method for the mapped class.

      Added in version 2.0.36.

    - dataclass_metadata –

      Specific to Declarative Dataclass Mapping, supplies metadata to be attached to the generated dataclass field.

      Added in version 2.0.42.

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

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

  Added in version 2.0.

class sqlalchemy.orm.MappedClassProtocol

  inherits from `typing.Protocol`

  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 sqlalchemy.orm.Mapper

  inherits from `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`

  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.

  Member Name | Description
  --- | ---
  __init__() | Direct constructor for a new `Mapper` object.
  add_properties() | Add the given dictionary of properties to this mapper, using add_property.
  add_property() | Add an individual MapperProperty to this mapper.
  all_orm_descriptors | A namespace of all `InspectionAttr` attributes associated with the mapped class.
  attrs | A namespace of all `MapperProperty` objects associated this mapper.
  base_mapper | The base-most `Mapper` in an inheritance chain.
  c | A synonym for `Mapper.columns`.
  cascade_iterator() | Iterate each element and its mapper in an object graph, for all relationships that meet the given cascade rule.
  class_ | The class to which this `Mapper` is mapped.
  class_manager | The `ClassManager` which maintains event listeners and class-bound descriptors for this `Mapper`.
  column_attrs | Return a namespace of all `ColumnProperty` properties maintained by this `Mapper`.
  columns | A collection of `Column` or other scalar expression objects maintained by this `Mapper`.
  common_parent() | Return true if the given mapper shares a common inherited parent as this mapper.
  composites | Return a namespace of all `Composite` properties maintained by this `Mapper`.
  concrete | Represent `True` if this `Mapper` is a concrete inheritance mapper.
  configured | Represent `True` if this `Mapper` has been configured.
  get_property() | return a MapperProperty associated with the given key.
  get_property_by_column() | Given a `Column` object, return the `MapperProperty` which maps this column.
  identity_key_from_instance() | Return the identity key for the given instance, based on its primary key attributes.
  identity_key_from_primary_key() | Return an identity-map key for use in storing/retrieving an item from an identity map.
  identity_key_from_row() | Return an identity-map key for use in storing/retrieving an item from the identity map.
  inherits | References the `Mapper` which this `Mapper` inherits from, if any.
  is_mapper | Part of the inspection API.
  is_sibling() | return true if the other mapper is an inheriting sibling to this one. common parent but different branch
  isa() | Return True if the this mapper inherits from the given mapper.
  local_table | The immediate `FromClause` to which this `Mapper` refers.
  mapped_table |
  non_primary | 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.
  persist_selectable | The `FromClause` to which this `Mapper` is mapped.
  polymorphic_identity | Represent an identifier which is matched against the `Mapper.polymorphic_on` column during result row loading.
  polymorphic_iterator() | Iterate through the collection including this mapper and all descendant mappers.
  polymorphic_map | A mapping of “polymorphic identity” identifiers mapped to `Mapper` instances, within an inheritance scenario.
  polymorphic_on | The `Column` or SQL expression specified as the `polymorphic_on` argument for this `Mapper`, within an inheritance scenario.
  primary_key | An iterable containing the collection of `Column` objects which comprise the ‘primary key’ of the mapped table, from the perspective of this `Mapper`.
  primary_key_from_instance() | Return the list of primary key values for the given instance.
  primary_mapper() | Return the primary mapper corresponding to this mapper’s class key (class).
  relationships | A namespace of all `Relationship` properties maintained by this `Mapper`.
  self_and_descendants | The collection including this mapper and all descendant mappers.
  single | Represent `True` if this `Mapper` is a single table inheritance mapper.
  synonyms | Return a namespace of all `Synonym` properties maintained by this `Mapper`.
  tables | A sequence containing the collection of `Table` or `TableClause` objects which this `Mapper` is aware of.
  validators | An immutable dictionary of attributes which have been decorated using the `validates()` decorator.
  with_polymorphic_mappers | The list of `Mapper` objects included in the default “polymorphic” query.

  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.

        Added 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.

        The `Mapper.allow_partial_pks` parameter also indicates to the ORM relationship lazy loader, when loading a many-to-one related object, if a composite primary key that has partial NULL values should result in an attempt to load from the database, or if a load attempt is not necessary.

        Added in version 2.0.36: `Mapper.allow_partial_pks` is consulted by the relationship lazy loader strategy, such that when set to False, a SELECT for a composite primary key that has partial NULL values will not be emitted.

      - 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.

        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.

        Added 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()`.

  property 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, 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.

  property 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

  property mapper: Mapper[_O]

    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[ColumnElement[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.

  property selectable: FromClause

    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.

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.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

  Added in version 2.0.10.

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.reconstructor(fn: _Fn) → _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.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.

  Added in version 1.4.

  See also

  ORM Mapped Class Overview - overview of class mapping styles.

  Member Name | Description
  --- | ---
  __init__() | Construct a new `registry`
  as_declarative_base() | Class decorator which will invoke `registry.generate_base()` for a given base class.
  configure() | Configure all as-yet unconfigured mappers in this `registry`.
  dispose() | Dispose of all mappers in this `registry`.
  generate_base() | Generate a declarative base class.
  map_declaratively() | Map a class declaratively.
  map_imperatively() | Map a class imperatively.
  mapped() | Class decorator that will apply the Declarative mapping process to a given class.
  mapped_as_dataclass() | Class decorator that will apply the Declarative mapping process to a given class, and additionally convert the class to be a Python dataclass.
  update_type_annotation_map() | update the `registry.type_annotation_map` with new values.

  method `sqlalchemy.orm.registry.`__init__(*, metadata: MetaData | None = None, class_registry: clsregistry._ClsRegistryType | None = None, type_annotation_map: _TypeAnnotationMapType | None = 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.

        Added 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()`

    Added 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.

    Added 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

    `mapped_as_dataclass()` - functional version that may provide better compatibility with mypy

    Added in version 2.0.

  property mappers: FrozenSet[Mapper[Any]]

    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.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.