Release: 2.0.30 | Release Date: May 5, 2024

# SQLAlchemy 2.0 Documentation

# Collection Customization and API Details

The `relationship()` function defines a linkage between two classes. When the linkage defines a one-to-many or many-to-many relationship, it’s represented as a Python collection when objects are loaded and manipulated. This section presents additional information about collection configuration and techniques.

## Customizing Collection Access

Mapping a one-to-many or many-to-many relationship results in a collection of values accessible through an attribute on the parent instance. The two common collection types for these are `list` and `set`, which in Declarative mappings that use `Mapped` is established by using the collection type within the `Mapped` container, as demonstrated in the `Parent.children` collection below where `list` is used:

    from sqlalchemy import ForeignKey

    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import relationship

    class Base(DeclarativeBase):
        pass

    class Parent(Base):
        __tablename__ = "parent"

        parent_id: Mapped[int] = mapped_column(primary_key=True)

        # use a list
        children: Mapped[List["Child"]] = relationship()

    class Child(Base):
        __tablename__ = "child"

        child_id: Mapped[int] = mapped_column(primary_key=True)
        parent_id: Mapped[int] = mapped_column(ForeignKey("parent.id"))

Or for a `set`, illustrated in the same `Parent.children` collection:

    from typing import Set
    from sqlalchemy import ForeignKey

    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import relationship

    class Base(DeclarativeBase):
        pass

    class Parent(Base):
        __tablename__ = "parent"

        parent_id: Mapped[int] = mapped_column(primary_key=True)

        # use a set
        children: Mapped[Set["Child"]] = relationship()

    class Child(Base):
        __tablename__ = "child"

        child_id: Mapped[int] = mapped_column(primary_key=True)
        parent_id: Mapped[int] = mapped_column(ForeignKey("parent.id"))

Note

If using Python 3.7 or 3.8, annotations for collections need to use `typing.List` or `typing.Set`, e.g. `Mapped[List["Child"]]` or `Mapped[Set["Child"]]`; the `list` and `set` Python built-ins don’t yet support generic annotation in these Python versions, such as:

    from typing import List

    class Parent(Base):
        __tablename__ = "parent"

        parent_id: Mapped[int] = mapped_column(primary_key=True)

        # use a List, Python 3.8 and earlier
        children: Mapped[List["Child"]] = relationship()

When using mappings without the `Mapped` annotation, such as when using imperative mappings or untyped Python code, as well as in a few special cases, the collection class for a `relationship()` can always be specified directly using the `relationship.collection_class` parameter:

    # non-annotated mapping

    class Parent(Base):
        __tablename__ = "parent"

        parent_id = mapped_column(Integer, primary_key=True)

        children = relationship("Child", collection_class=set)

    class Child(Base):
        __tablename__ = "child"

        child_id = mapped_column(Integer, primary_key=True)
        parent_id = mapped_column(ForeignKey("parent.id"))

In the absence of `relationship.collection_class` or `Mapped`, the default collection type is `list`.

Beyond `list` and `set` builtins, there is also support for two varieties of dictionary, described below at Dictionary Collections. There is also support for any arbitrary mutable sequence type can be set up as the target collection, with some additional configuration steps; this is described in the section Custom Collection Implementations.

### Dictionary Collections

A little extra detail is needed when using a dictionary as a collection. This because objects are always loaded from the database as lists, and a key-generation strategy must be available to populate the dictionary correctly. The `attribute_keyed_dict()` function is by far the most common way to achieve a simple dictionary collection. It produces a dictionary class that will apply a particular attribute of the mapped class as a key. Below we map an `Item` class containing a dictionary of `Note` items keyed to the `Note.keyword` attribute. When using `attribute_keyed_dict()`, the `Mapped` annotation may be typed using the `KeyFuncDict` or just plain `dict` as illustrated in the following example. However, the `relationship.collection_class` parameter is required in this case so that the `attribute_keyed_dict()` may be appropriately parametrized:

    from typing import Dict
    from typing import Optional

    from sqlalchemy import ForeignKey
    from sqlalchemy.orm import attribute_keyed_dict
    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import relationship

    class Base(DeclarativeBase):
        pass

    class Item(Base):
        __tablename__ = "item"

        id: Mapped[int] = mapped_column(primary_key=True)

        notes: Mapped[Dict[str, "Note"]] = relationship(
            collection_class=attribute_keyed_dict("keyword"),
            cascade="all, delete-orphan",
        )

    class Note(Base):
        __tablename__ = "note"

        id: Mapped[int] = mapped_column(primary_key=True)
        item_id: Mapped[int] = mapped_column(ForeignKey("item.id"))
        keyword: Mapped[str]
        text: Mapped[Optional[str]]

        def __init__(self, keyword: str, text: str):
            self.keyword = keyword
            self.text = text

`Item.notes` is then a dictionary:

    >>> item = Item()
    >>> item.notes["a"] = Note("a", "atext")
    >>> item.notes.items()
    {'a': <__main__.Note object at 0x2eaaf0>}

`attribute_keyed_dict()` will ensure that the `.keyword` attribute of each `Note` complies with the key in the dictionary. Such as, when assigning to `Item.notes`, the dictionary key we supply must match that of the actual `Note` object:

    item = Item()
    item.notes = {
        "a": Note("a", "atext"),
        "b": Note("b", "btext"),
    }

The attribute which `attribute_keyed_dict()` uses as a key does not need to be mapped at all! Using a regular Python `@property` allows virtually any detail or combination of details about the object to be used as the key, as below when we establish it as a tuple of `Note.keyword` and the first ten letters of the `Note.text` field:

    class Item(Base):
        __tablename__ = "item"

        id: Mapped[int] = mapped_column(primary_key=True)

        notes: Mapped[Dict[str, "Note"]] = relationship(
            collection_class=attribute_keyed_dict("note_key"),
            back_populates="item",
            cascade="all, delete-orphan",
        )

    class Note(Base):
        __tablename__ = "note"

        id: Mapped[int] = mapped_column(primary_key=True)
        item_id: Mapped[int] = mapped_column(ForeignKey("item.id"))
        keyword: Mapped[str]
        text: Mapped[str]

        item: Mapped["Item"] = relationship()

        @property
        def note_key(self):
            return (self.keyword, self.text[0:10])

        def __init__(self, keyword: str, text: str):
            self.keyword = keyword
            self.text = text

Above we added a `Note.item` relationship, with a bi-directional `relationship.back_populates` configuration. Assigning to this reverse relationship, the `Note` is added to the `Item.notes` dictionary and the key is generated for us automatically:

    >>> item = Item()
    >>> n1 = Note("a", "atext")
    >>> n1.item = item
    >>> item.notes
    {('a', 'atext'): <__main__.Note object at 0x2eaaf0>}

Other built-in dictionary types include `column_keyed_dict()`, which is almost like `attribute_keyed_dict()` except given the `Column` object directly:

    from sqlalchemy.orm import column_keyed_dict

    class Item(Base):
        __tablename__ = "item"

        id: Mapped[int] = mapped_column(primary_key=True)

        notes: Mapped[Dict[str, "Note"]] = relationship(
            collection_class=column_keyed_dict(Note.__table__.c.keyword),
            cascade="all, delete-orphan",
        )

as well as `mapped_collection()` which is passed any callable function. Note that it’s usually easier to use `attribute_keyed_dict()` along with a `@property` as mentioned earlier:

    from sqlalchemy.orm import mapped_collection

    class Item(Base):
        __tablename__ = "item"

        id: Mapped[int] = mapped_column(primary_key=True)

        notes: Mapped[Dict[str, "Note"]] = relationship(
            collection_class=mapped_collection(lambda note: note.text[0:10]),
            cascade="all, delete-orphan",
        )

Dictionary mappings are often combined with the “Association Proxy” extension to produce streamlined dictionary views. See Proxying to Dictionary Based Collections and Composite Association Proxies for examples.

#### Dealing with Key Mutations and back-populating for Dictionary collections

When using `attribute_keyed_dict()`, the “key” for the dictionary is taken from an attribute on the target object. Changes to this key are not tracked. This means that the key must be assigned towards when it is first used, and if the key changes, the collection will not be mutated. A typical example where this might be an issue is when relying upon backrefs to populate an attribute mapped collection. Given the following:

    class A(Base):
        __tablename__ = "a"

        id: Mapped[int] = mapped_column(primary_key=True)

        bs: Mapped[Dict[str, "B"]] = relationship(
            collection_class=attribute_keyed_dict("data"),
            back_populates="a",
        )

    class B(Base):
        __tablename__ = "b"

        id: Mapped[int] = mapped_column(primary_key=True)
        a_id: Mapped[int] = mapped_column(ForeignKey("a.id"))
        data: Mapped[str]

        a: Mapped["A"] = relationship(back_populates="bs")

Above, if we create a `B()` that refers to a specific `A()`, the back populates will then add the `B()` to the `A.bs` collection, however if the value of `B.data` is not set yet, the key will be `None`:

    >>> a1 = A()
    >>> b1 = B(a=a1)
    >>> a1.bs
    {None: <test3.B object at 0x7f7b1023ef70>}

Setting `b1.data` after the fact does not update the collection:

    >>> b1.data = "the key"
    >>> a1.bs
    {None: <test3.B object at 0x7f7b1023ef70>}

This can also be seen if one attempts to set up `B()` in the constructor. The order of arguments changes the result:

    >>> B(a=a1, data="the key")
    <test3.B object at 0x7f7b10114280>
    >>> a1.bs
    {None: <test3.B object at 0x7f7b10114280>}

vs:

    >>> B(data="the key", a=a1)
    <test3.B object at 0x7f7b10114340>
    >>> a1.bs
    {'the key': <test3.B object at 0x7f7b10114340>}

If backrefs are being used in this way, ensure that attributes are populated in the correct order using an `__init__` method.

An event handler such as the following may also be used to track changes in the collection as well:

    from sqlalchemy import event
    from sqlalchemy.orm import attributes

    @event.listens_for(B.data, "set")
    def set_item(obj, value, previous, initiator):
        if obj.a is not None:
            previous = None if previous == attributes.NO_VALUE else previous
            obj.a.bs[value] = obj
            obj.a.bs.pop(previous)

## Custom Collection Implementations

You can use your own types for collections as well. In simple cases, inheriting from `list` or `set`, adding custom behavior, is all that’s needed. In other cases, special decorators are needed to tell SQLAlchemy more detail about how the collection operates.

Do I need a custom collection implementation?

In most cases not at all! The most common use cases for a “custom” collection is one that validates or marshals incoming values into a new form, such as a string that becomes a class instance, or one which goes a step beyond and represents the data internally in some fashion, presenting a “view” of that data on the outside of a different form.

For the first use case, the `validates()` decorator is by far the simplest way to intercept incoming values in all cases for the purposes of validation and simple marshaling. See Simple Validators for an example of this.

For the second use case, the Association Proxy extension is a well-tested, widely used system that provides a read/write “view” of a collection in terms of some attribute present on the target object. As the target attribute can be a `@property` that returns virtually anything, a wide array of “alternative” views of a collection can be constructed with just a few functions. This approach leaves the underlying mapped collection unaffected and avoids the need to carefully tailor collection behavior on a method-by-method basis.

Customized collections are useful when the collection needs to have special behaviors upon access or mutation operations that can’t otherwise be modeled externally to the collection. They can of course be combined with the above two approaches.

Collections in SQLAlchemy are transparently instrumented. Instrumentation means that normal operations on the collection are tracked and result in changes being written to the database at flush time. Additionally, collection operations can fire events which indicate some secondary operation must take place. Examples of a secondary operation include saving the child item in the parent’s `Session` (i.e. the `save-update` cascade), as well as synchronizing the state of a bi-directional relationship (i.e. a `backref()`).

The collections package understands the basic interface of lists, sets and dicts and will automatically apply instrumentation to those built-in types and their subclasses. Object-derived types that implement a basic collection interface are detected and instrumented via duck-typing:

    class ListLike:
        def __init__(self):
            self.data = []

        def append(self, item):
            self.data.append(item)

        def remove(self, item):
            self.data.remove(item)

        def extend(self, items):
            self.data.extend(items)

        def __iter__(self):
            return iter(self.data)

        def foo(self):
            return "foo"

`append`, `remove`, and `extend` are known members of `list`, and will be instrumented automatically. `__iter__` is not a mutator method and won’t be instrumented, and `foo` won’t be either.

Duck-typing (i.e. guesswork) isn’t rock-solid, of course, so you can be explicit about the interface you are implementing by providing an `__emulates__` class attribute:

    class SetLike:
        __emulates__ = set

        def __init__(self):
            self.data = set()

        def append(self, item):
            self.data.add(item)

        def remove(self, item):
            self.data.remove(item)

        def __iter__(self):
            return iter(self.data)

This class looks similar to a Python `list` (i.e. “list-like”) as it has an `append` method, but the `__emulates__` attribute forces it to be treated as a `set`. `remove` is known to be part of the set interface and will be instrumented.

But this class won’t work quite yet: a little glue is needed to adapt it for use by SQLAlchemy. The ORM needs to know which methods to use to append, remove and iterate over members of the collection. When using a type like `list` or `set`, the appropriate methods are well-known and used automatically when present. However the class above, which only roughly resembles a `set`, does not provide the expected `add` method, so we must indicate to the ORM the method that will instead take the place of the `add` method, in this case using a decorator `@collection.appender`; this is illustrated in the next section.

### Annotating Custom Collections via Decorators

Decorators can be used to tag the individual methods the ORM needs to manage collections. Use them when your class doesn’t quite meet the regular interface for its container type, or when you otherwise would like to use a different method to get the job done.

    from sqlalchemy.orm.collections import collection

    class SetLike:
        __emulates__ = set

        def __init__(self):
            self.data = set()

        @collection.appender
        def append(self, item):
            self.data.add(item)

        def remove(self, item):
            self.data.remove(item)

        def __iter__(self):
            return iter(self.data)

And that’s all that’s needed to complete the example. SQLAlchemy will add instances via the `append` method. `remove` and `__iter__` are the default methods for sets and will be used for removing and iteration. Default methods can be changed as well:

    from sqlalchemy.orm.collections import collection

    class MyList(list):
        @collection.remover
        def zark(self, item):
            # do something special...
            ...

        @collection.iterator
        def hey_use_this_instead_for_iteration(self): ...

There is no requirement to be “list-like” or “set-like” at all. Collection classes can be any shape, so long as they have the append, remove and iterate interface marked for SQLAlchemy’s use. Append and remove methods will be called with a mapped entity as the single argument, and iterator methods are called with no arguments and must return an iterator.

### Custom Dictionary-Based Collections

The `KeyFuncDict` class can be used as a base class for your custom types or as a mix-in to quickly add `dict` collection support to other classes. It uses a keying function to delegate to `__setitem__` and `__delitem__`:

    from sqlalchemy.orm.collections import KeyFuncDict

    class MyNodeMap(KeyFuncDict):
        """Holds 'Node' objects, keyed by the 'name' attribute."""

        def __init__(self, *args, **kw):
            super().__init__(keyfunc=lambda node: node.name)
            dict.__init__(self, *args, **kw)

When subclassing `KeyFuncDict`, user-defined versions of `__setitem__()` or `__delitem__()` should be decorated with `collection.internally_instrumented()`, if they call down to those same methods on `KeyFuncDict`. This because the methods on `KeyFuncDict` are already instrumented - calling them from within an already instrumented call can cause events to be fired off repeatedly, or inappropriately, leading to internal state corruption in rare cases:

    from sqlalchemy.orm.collections import KeyFuncDict, collection

    class MyKeyFuncDict(KeyFuncDict):
        """Use @internally_instrumented when your methods
        call down to already-instrumented methods.

        """

        @collection.internally_instrumented
        def __setitem__(self, key, value, _sa_initiator=None):
            # do something with key, value
            super(MyKeyFuncDict, self).__setitem__(key, value, _sa_initiator)

        @collection.internally_instrumented
        def __delitem__(self, key, _sa_initiator=None):
            # do something with key
            super(MyKeyFuncDict, self).__delitem__(key, _sa_initiator)

The ORM understands the `dict` interface just like lists and sets, and will automatically instrument all “dict-like” methods if you choose to subclass `dict` or provide dict-like collection behavior in a duck-typed class. You must decorate appender and remover methods, however- there are no compatible methods in the basic dictionary interface for SQLAlchemy to use by default. Iteration will go through `values()` unless otherwise decorated.

### Instrumentation and Custom Types

Many custom types and existing library classes can be used as a entity collection type as-is without further ado. However, it is important to note that the instrumentation process will modify the type, adding decorators around methods automatically.

The decorations are lightweight and no-op outside of relationships, but they do add unneeded overhead when triggered elsewhere. When using a library class as a collection, it can be good practice to use the “trivial subclass” trick to restrict the decorations to just your usage in relationships. For example:

    class MyAwesomeList(some.great.library.AwesomeList):
        pass

    # ... relationship(..., collection_class=MyAwesomeList)

The ORM uses this approach for built-ins, quietly substituting a trivial subclass when a `list`, `set` or `dict` is used directly.

## Collection API

Object Name | Description
--- | ---
attribute_keyed_dict(attr_name, *, [ignore_unpopulated_attribute]) | A dictionary-based collection type with attribute-based keying.
attribute_mapped_collection | A dictionary-based collection type with attribute-based keying.
column_keyed_dict(mapping_spec, *, [ignore_unpopulated_attribute]) | A dictionary-based collection type with column-based keying.
column_mapped_collection | A dictionary-based collection type with column-based keying.
keyfunc_mapping(keyfunc, *, [ignore_unpopulated_attribute]) | A dictionary-based collection type with arbitrary keying.
KeyFuncDict | Base for ORM mapped dictionary classes.
mapped_collection | A dictionary-based collection type with arbitrary keying.
MappedCollection | Base for ORM mapped dictionary classes.

function sqlalchemy.orm.attribute_keyed_dict(attr_name: str, *, ignore_unpopulated_attribute: bool = False) → Type[KeyFuncDict[Any, Any]]

  A dictionary-based collection type with attribute-based keying.

  Changed in version 2.0: Renamed `attribute_mapped_collection` to `attribute_keyed_dict()`.

  Returns a `KeyFuncDict` factory which will produce new dictionary keys based on the value of a particular named attribute on ORM mapped instances to be added to the dictionary.

  Note

  the value of the target attribute must be assigned with its value at the time that the object is being added to the dictionary collection. Additionally, changes to the key attribute are not tracked, which means the key in the dictionary is not automatically synchronized with the key value on the target object itself. See Dealing with Key Mutations and back-populating for Dictionary collections for further details.

  See also

  Dictionary Collections - background on use

  Parameters:

    - attr_name – string name of an ORM-mapped attribute on the mapped class, the value of which on a particular instance is to be used as the key for a new dictionary entry for that instance.

    - ignore_unpopulated_attribute –

      if True, and the target attribute on an object is not populated at all, the operation will be silently skipped. By default, an error is raised.

      New in version 2.0: an error is raised by default if the attribute being used for the dictionary key is determined that it was never populated with any value. The `attribute_keyed_dict.ignore_unpopulated_attribute` parameter may be set which will instead indicate that this condition should be ignored, and the append operation silently skipped. This is in contrast to the behavior of the 1.x series which would erroneously populate the value in the dictionary with an arbitrary key value of `None`.

function sqlalchemy.orm.column_keyed_dict(mapping_spec: Type[_KT] | Callable[[_KT], _VT], *, ignore_unpopulated_attribute: bool = False) → Type[KeyFuncDict[_KT, _KT]]

  A dictionary-based collection type with column-based keying.

  Changed in version 2.0: Renamed `column_mapped_collection` to `column_keyed_dict`.

  Returns a `KeyFuncDict` factory which will produce new dictionary keys based on the value of a particular `Column`-mapped attribute on ORM mapped instances to be added to the dictionary.

  Note

  the value of the target attribute must be assigned with its value at the time that the object is being added to the dictionary collection. Additionally, changes to the key attribute are not tracked, which means the key in the dictionary is not automatically synchronized with the key value on the target object itself. See Dealing with Key Mutations and back-populating for Dictionary collections for further details.

  See also

  Dictionary Collections - background on use

  Parameters:

    - mapping_spec – a `Column` object that is expected to be mapped by the target mapper to a particular attribute on the mapped class, the value of which on a particular instance is to be used as the key for a new dictionary entry for that instance.

    - ignore_unpopulated_attribute –

      if True, and the mapped attribute indicated by the given `Column` target attribute on an object is not populated at all, the operation will be silently skipped. By default, an error is raised.

      New in version 2.0: an error is raised by default if the attribute being used for the dictionary key is determined that it was never populated with any value. The `column_keyed_dict.ignore_unpopulated_attribute` parameter may be set which will instead indicate that this condition should be ignored, and the append operation silently skipped. This is in contrast to the behavior of the 1.x series which would erroneously populate the value in the dictionary with an arbitrary key value of `None`.

function sqlalchemy.orm.keyfunc_mapping(keyfunc: _F, *, ignore_unpopulated_attribute: bool = False) → Type[KeyFuncDict[_KT, Any]]

  A dictionary-based collection type with arbitrary keying.

  Changed in version 2.0: Renamed `mapped_collection` to `keyfunc_mapping()`.

  Returns a `KeyFuncDict` factory with a keying function generated from keyfunc, a callable that takes an entity and returns a key value.

  Note

  the given keyfunc is called only once at the time that the target object is being added to the collection. Changes to the effective value returned by the function are not tracked.

  See also

  Dictionary Collections - background on use

  Parameters:

    - keyfunc – a callable that will be passed the ORM-mapped instance which should then generate a new key to use in the dictionary. If the value returned is `LoaderCallableStatus.NO_VALUE`, an error is raised.

    - ignore_unpopulated_attribute –

      if True, and the callable returns `LoaderCallableStatus.NO_VALUE` for a particular instance, the operation will be silently skipped. By default, an error is raised.

      New in version 2.0: an error is raised by default if the callable being used for the dictionary key returns `LoaderCallableStatus.NO_VALUE`, which in an ORM attribute context indicates an attribute that was never populated with any value. The `mapped_collection.ignore_unpopulated_attribute` parameter may be set which will instead indicate that this condition should be ignored, and the append operation silently skipped. This is in contrast to the behavior of the 1.x series which would erroneously populate the value in the dictionary with an arbitrary key value of `None`.

sqlalchemy.orm.attribute_mapped_collection = <function attribute_keyed_dict>

  A dictionary-based collection type with attribute-based keying.

  Changed in version 2.0: Renamed `attribute_mapped_collection` to `attribute_keyed_dict()`.

  Returns a `KeyFuncDict` factory which will produce new dictionary keys based on the value of a particular named attribute on ORM mapped instances to be added to the dictionary.

  Note

  the value of the target attribute must be assigned with its value at the time that the object is being added to the dictionary collection. Additionally, changes to the key attribute are not tracked, which means the key in the dictionary is not automatically synchronized with the key value on the target object itself. See Dealing with Key Mutations and back-populating for Dictionary collections for further details.

  See also

  Dictionary Collections - background on use

  Parameters:

    - attr_name – string name of an ORM-mapped attribute on the mapped class, the value of which on a particular instance is to be used as the key for a new dictionary entry for that instance.

    - ignore_unpopulated_attribute –

      if True, and the target attribute on an object is not populated at all, the operation will be silently skipped. By default, an error is raised.

      New in version 2.0: an error is raised by default if the attribute being used for the dictionary key is determined that it was never populated with any value. The `attribute_keyed_dict.ignore_unpopulated_attribute` parameter may be set which will instead indicate that this condition should be ignored, and the append operation silently skipped. This is in contrast to the behavior of the 1.x series which would erroneously populate the value in the dictionary with an arbitrary key value of `None`.

sqlalchemy.orm.column_mapped_collection = <function column_keyed_dict>

  A dictionary-based collection type with column-based keying.

  Changed in version 2.0: Renamed `column_mapped_collection` to `column_keyed_dict`.

  Returns a `KeyFuncDict` factory which will produce new dictionary keys based on the value of a particular `Column`-mapped attribute on ORM mapped instances to be added to the dictionary.

  Note

  the value of the target attribute must be assigned with its value at the time that the object is being added to the dictionary collection. Additionally, changes to the key attribute are not tracked, which means the key in the dictionary is not automatically synchronized with the key value on the target object itself. See Dealing with Key Mutations and back-populating for Dictionary collections for further details.

  See also

  Dictionary Collections - background on use

  Parameters:

    - mapping_spec – a `Column` object that is expected to be mapped by the target mapper to a particular attribute on the mapped class, the value of which on a particular instance is to be used as the key for a new dictionary entry for that instance.

    - ignore_unpopulated_attribute –

      if True, and the mapped attribute indicated by the given `Column` target attribute on an object is not populated at all, the operation will be silently skipped. By default, an error is raised.

      New in version 2.0: an error is raised by default if the attribute being used for the dictionary key is determined that it was never populated with any value. The `column_keyed_dict.ignore_unpopulated_attribute` parameter may be set which will instead indicate that this condition should be ignored, and the append operation silently skipped. This is in contrast to the behavior of the 1.x series which would erroneously populate the value in the dictionary with an arbitrary key value of `None`.

sqlalchemy.orm.mapped_collection = <function keyfunc_mapping>

  A dictionary-based collection type with arbitrary keying.

  Changed in version 2.0: Renamed `mapped_collection` to `keyfunc_mapping()`.

  Returns a `KeyFuncDict` factory with a keying function generated from keyfunc, a callable that takes an entity and returns a key value.

  Note

  the given keyfunc is called only once at the time that the target object is being added to the collection. Changes to the effective value returned by the function are not tracked.

  See also

  Dictionary Collections - background on use

  Parameters:

    - keyfunc – a callable that will be passed the ORM-mapped instance which should then generate a new key to use in the dictionary. If the value returned is `LoaderCallableStatus.NO_VALUE`, an error is raised.

    - ignore_unpopulated_attribute –

      if True, and the callable returns `LoaderCallableStatus.NO_VALUE` for a particular instance, the operation will be silently skipped. By default, an error is raised.

      New in version 2.0: an error is raised by default if the callable being used for the dictionary key returns `LoaderCallableStatus.NO_VALUE`, which in an ORM attribute context indicates an attribute that was never populated with any value. The `mapped_collection.ignore_unpopulated_attribute` parameter may be set which will instead indicate that this condition should be ignored, and the append operation silently skipped. This is in contrast to the behavior of the 1.x series which would erroneously populate the value in the dictionary with an arbitrary key value of `None`.

class sqlalchemy.orm.KeyFuncDict

  Base for ORM mapped dictionary classes.

  Extends the `dict` type with additional methods needed by SQLAlchemy ORM collection classes. Use of `KeyFuncDict` is most directly by using the `attribute_keyed_dict()` or `column_keyed_dict()` class factories. `KeyFuncDict` may also serve as the base for user-defined custom dictionary classes.

  Changed in version 2.0: Renamed `MappedCollection` to `KeyFuncDict`.

  See also

  `attribute_keyed_dict()`

  `column_keyed_dict()`

  Dictionary Collections

  Custom Collection Implementations

  Members

  __init__(), clear(), pop(), popitem(), remove(), set(), setdefault(), update()

  Class signature

  class `sqlalchemy.orm.KeyFuncDict` (`builtins.dict`, `typing.Generic`)

  method `sqlalchemy.orm.KeyFuncDict.`__init__(keyfunc: _F, *dict_args: Any, ignore_unpopulated_attribute: bool = False) → None

    Create a new collection with keying provided by keyfunc.

    keyfunc may be any callable that takes an object and returns an object for use as a dictionary key.

    The keyfunc will be called every time the ORM needs to add a member by value-only (such as when loading instances from the database) or remove a member. The usual cautions about dictionary keying apply- `keyfunc(object)` should return the same output for the life of the collection. Keying based on mutable properties can result in unreachable instances “lost” in the collection.

  method `sqlalchemy.orm.KeyFuncDict.`clear() → None. Remove all items from D.
  method `sqlalchemy.orm.KeyFuncDict.`pop(k[, d]) → v, remove specified key and return the corresponding value.

    If the key is not found, return the default if given; otherwise, raise a KeyError.

  method `sqlalchemy.orm.KeyFuncDict.`popitem()

    Remove and return a (key, value) pair as a 2-tuple.

    Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

  method `sqlalchemy.orm.KeyFuncDict.`remove(value: _KT, _sa_initiator: AttributeEventToken | Literal[None, False] = None) → None

    Remove an item by value, consulting the keyfunc for the key.

  method `sqlalchemy.orm.KeyFuncDict.`set(value: _KT, _sa_initiator: AttributeEventToken | Literal[None, False] = None) → None

    Add an item by value, consulting the keyfunc for the key.

  method `sqlalchemy.orm.KeyFuncDict.`setdefault(key, default=None)

    Insert key with a value of default if key is not in the dictionary.

    Return the value for key if key is in the dictionary, else default.

  method `sqlalchemy.orm.KeyFuncDict.`update([E, ]**F) → None. Update D from dict/iterable E and F.

    If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

sqlalchemy.orm.MappedCollection = <class 'sqlalchemy.orm.mapped_collection.KeyFuncDict'>

  Base for ORM mapped dictionary classes.

  Extends the `dict` type with additional methods needed by SQLAlchemy ORM collection classes. Use of `KeyFuncDict` is most directly by using the `attribute_keyed_dict()` or `column_keyed_dict()` class factories. `KeyFuncDict` may also serve as the base for user-defined custom dictionary classes.

  Changed in version 2.0: Renamed `MappedCollection` to `KeyFuncDict`.

  See also

  `attribute_keyed_dict()`

  `column_keyed_dict()`

  Dictionary Collections

  Custom Collection Implementations

## Collection Internals

Object Name | Description
--- | ---
bulk_replace(values, existing_adapter, new_adapter[, initiator]) | Load a new collection, firing events based on prior like membership.
collection | Decorators for entity collection classes.
collection_adapter | Return a callable object that fetches the given attribute(s) from its operand. After f = attrgetter(‘name’), the call f(r) returns r.name. After g = attrgetter(‘name’, ‘date’), the call g(r) returns (r.name, r.date). After h = attrgetter(‘name.first’, ‘name.last’), the call h(r) returns (r.name.first, r.name.last).
CollectionAdapter | Bridges between the ORM and arbitrary Python collections.
InstrumentedDict | An instrumented version of the built-in dict.
InstrumentedList | An instrumented version of the built-in list.
InstrumentedSet | An instrumented version of the built-in set.
prepare_instrumentation(factory) | Prepare a callable for future use as a collection class factory.

function sqlalchemy.orm.collections.bulk_replace(values, existing_adapter, new_adapter, initiator=None)

  Load a new collection, firing events based on prior like membership.

  Appends instances in `values` onto the `new_adapter`. Events will be fired for any instance not present in the `existing_adapter`. Any instances in `existing_adapter` not present in `values` will have remove events fired upon them.

  Parameters:

    - values – An iterable of collection member instances

    - existing_adapter – A `CollectionAdapter` of instances to be replaced

    - new_adapter – An empty `CollectionAdapter` to load with `values`

class sqlalchemy.orm.collections.collection

  Decorators for entity collection classes.

  The decorators fall into two groups: annotations and interception recipes.

  The annotating decorators (appender, remover, iterator, converter, internally_instrumented) indicate the method’s purpose and take no arguments. They are not written with parens:

      @collection.appender
      def append(self, append): ...

  The recipe decorators all require parens, even those that take no arguments:

  Members

  adds(), appender(), converter(), internally_instrumented(), iterator(), remover(), removes(), removes_return(), replaces()

      @collection.adds('entity')
      def insert(self, position, entity): ...

      @collection.removes_return()
      def popitem(self): ...

  method `sqlalchemy.orm.collections.collection.`static adds(arg)

    Mark the method as adding an entity to the collection.

    Adds “add to collection” handling to the method. The decorator argument indicates which method argument holds the SQLAlchemy-relevant value. Arguments can be specified positionally (i.e. integer) or by name:

        @collection.adds(1)
        def push(self, item): ...

        @collection.adds('entity')
        def do_stuff(self, thing, entity=None): ...

  method `sqlalchemy.orm.collections.collection.`static appender(fn)

    Tag the method as the collection appender.

    The appender method is called with one positional argument: the value to append. The method will be automatically decorated with ‘adds(1)’ if not already decorated:

        @collection.appender
        def add(self, append): ...

        # or, equivalently
        @collection.appender
        @collection.adds(1)
        def add(self, append): ...

        # for mapping type, an 'append' may kick out a previous value
        # that occupies that slot.  consider d['a'] = 'foo'- any previous
        # value in d['a'] is discarded.
        @collection.appender
        @collection.replaces(1)
        def add(self, entity):
            key = some_key_func(entity)
            previous = None
            if key in self:
                previous = self[key]
            self[key] = entity
            return previous

    If the value to append is not allowed in the collection, you may raise an exception. Something to remember is that the appender will be called for each object mapped by a database query. If the database contains rows that violate your collection semantics, you will need to get creative to fix the problem, as access via the collection will not work.

    If the appender method is internally instrumented, you must also receive the keyword argument ‘_sa_initiator’ and ensure its promulgation to collection events.

  method `sqlalchemy.orm.collections.collection.`static converter(fn)

    Tag the method as the collection converter.

    Deprecated since version 1.3: The `collection.converter()` handler is deprecated and will be removed in a future release. Please refer to the `bulk_replace` listener interface in conjunction with the `listen()` function.

    This optional method will be called when a collection is being replaced entirely, as in:

        myobj.acollection = [newvalue1, newvalue2]

    The converter method will receive the object being assigned and should return an iterable of values suitable for use by the `appender` method. A converter must not assign values or mutate the collection, its sole job is to adapt the value the user provides into an iterable of values for the ORM’s use.

    The default converter implementation will use duck-typing to do the conversion. A dict-like collection will be convert into an iterable of dictionary values, and other types will simply be iterated:

        @collection.converter
        def convert(self, other): ...

    If the duck-typing of the object does not match the type of this collection, a TypeError is raised.

    Supply an implementation of this method if you want to expand the range of possible types that can be assigned in bulk or perform validation on the values about to be assigned.

  method `sqlalchemy.orm.collections.collection.`static internally_instrumented(fn)

    Tag the method as instrumented.

    This tag will prevent any decoration from being applied to the method. Use this if you are orchestrating your own calls to `collection_adapter()` in one of the basic SQLAlchemy interface methods, or to prevent an automatic ABC method decoration from wrapping your implementation:

        # normally an 'extend' method on a list-like class would be
        # automatically intercepted and re-implemented in terms of
        # SQLAlchemy events and append().  your implementation will
        # never be called, unless:
        @collection.internally_instrumented
        def extend(self, items): ...

  method `sqlalchemy.orm.collections.collection.`static iterator(fn)

    Tag the method as the collection remover.

    The iterator method is called with no arguments. It is expected to return an iterator over all collection members:

        @collection.iterator
        def __iter__(self): ...

  method `sqlalchemy.orm.collections.collection.`static remover(fn)

    Tag the method as the collection remover.

    The remover method is called with one positional argument: the value to remove. The method will be automatically decorated with `removes_return()` if not already decorated:

        @collection.remover
        def zap(self, entity): ...

        # or, equivalently
        @collection.remover
        @collection.removes_return()
        def zap(self, ): ...

    If the value to remove is not present in the collection, you may raise an exception or return None to ignore the error.

    If the remove method is internally instrumented, you must also receive the keyword argument ‘_sa_initiator’ and ensure its promulgation to collection events.

  method `sqlalchemy.orm.collections.collection.`static removes(arg)

    Mark the method as removing an entity in the collection.

    Adds “remove from collection” handling to the method. The decorator argument indicates which method argument holds the SQLAlchemy-relevant value to be removed. Arguments can be specified positionally (i.e. integer) or by name:

        @collection.removes(1)
        def zap(self, item): ...

    For methods where the value to remove is not known at call-time, use collection.removes_return.

  method `sqlalchemy.orm.collections.collection.`static removes_return()

    Mark the method as removing an entity in the collection.

    Adds “remove from collection” handling to the method. The return value of the method, if any, is considered the value to remove. The method arguments are not inspected:

        @collection.removes_return()
        def pop(self): ...

    For methods where the value to remove is known at call-time, use collection.remove.

  method `sqlalchemy.orm.collections.collection.`static replaces(arg)

    Mark the method as replacing an entity in the collection.

    Adds “add to collection” and “remove from collection” handling to the method. The decorator argument indicates which method argument holds the SQLAlchemy-relevant value to be added, and return value, if any will be considered the value to remove.

    Arguments can be specified positionally (i.e. integer) or by name:

        @collection.replaces(2)
        def __setitem__(self, index, item): ...

sqlalchemy.orm.collections.collection_adapter = operator.attrgetter('_sa_adapter')

  Return a callable object that fetches the given attribute(s) from its operand. After f = attrgetter(‘name’), the call f(r) returns r.name. After g = attrgetter(‘name’, ‘date’), the call g(r) returns (r.name, r.date). After h = attrgetter(‘name.first’, ‘name.last’), the call h(r) returns (r.name.first, r.name.last).

class sqlalchemy.orm.collections.CollectionAdapter

  Bridges between the ORM and arbitrary Python collections.

  Proxies base-level collection operations (append, remove, iterate) to the underlying Python collection, and emits add/remove events for entities entering or leaving the collection.

  The ORM uses `CollectionAdapter` exclusively for interaction with entity collections.

class sqlalchemy.orm.collections.InstrumentedDict

  An instrumented version of the built-in dict.

  Class signature

  class `sqlalchemy.orm.collections.InstrumentedDict` (`builtins.dict`, `typing.Generic`)

class sqlalchemy.orm.collections.InstrumentedList

  An instrumented version of the built-in list.

  Class signature

  class `sqlalchemy.orm.collections.InstrumentedList` (`builtins.list`, `typing.Generic`)

class sqlalchemy.orm.collections.InstrumentedSet

  An instrumented version of the built-in set.

  Class signature

  class `sqlalchemy.orm.collections.InstrumentedSet` (`builtins.set`, `typing.Generic`)

function sqlalchemy.orm.collections.prepare_instrumentation(factory: Type[Collection[Any]] | Callable[[], _AdaptedCollectionProtocol]) → Callable[[], _AdaptedCollectionProtocol]

  Prepare a callable for future use as a collection class factory.

  Given a collection class factory (either a type or no-arg callable), return another factory that will produce compatible instances when called.

  This function is responsible for converting collection_class=list into the run-time behavior of collection_class=InstrumentedList.