.. highlight:: python

.. _custom_collections_toplevel:

.. currentmodule:: sqlalchemy.orm

========================================
Collection Customization and API Details
========================================

The :func:`_orm.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.



.. _custom_collections:

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
:ref:`Declarative <orm_declarative_styles_toplevel>` mappings that use
:class:`_orm.Mapped` is established by using the collection type within
the :class:`_orm.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 :class:`_orm.Mapped` annotation, such as when
using :ref:`imperative mappings <orm_imperative_mapping>` or untyped
Python code, as well as in a few special cases, the collection class for a
:func:`_orm.relationship` can always be specified directly using the
:paramref:`_orm.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 :paramref:`_orm.relationship.collection_class`
or :class:`_orm.Mapped`, the default collection type is ``list``.

Beyond ``list`` and ``set`` builtins, there is also support for two varieties of
dictionary, described below at :ref:`orm_dictionary_collection`. 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 :ref:`orm_custom_collection`.


.. _orm_dictionary_collection:

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
:func:`.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 :func:`.attribute_keyed_dict`, the :class:`_orm.Mapped`
annotation may be typed using the :class:`_orm.KeyFuncDict`
or just plain ``dict`` as illustrated in the following example.   However,
the :paramref:`_orm.relationship.collection_class` parameter
is required in this case so that the :func:`.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>}

:func:`.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 :func:`.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
:paramref:`_orm.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 :func:`.column_keyed_dict`,
which is almost like :func:`.attribute_keyed_dict` except given the :class:`_schema.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 :func:`.mapped_collection` which is passed any callable function.
Note that it's usually easier to use :func:`.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 :ref:`proxying_dictionaries` and :ref:`composite_association_proxy`
for examples.

.. _key_collections_mutations:

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

When using :func:`.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)

.. _orm_custom_collection:

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.

.. topic:: 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 :func:`_orm.validates` decorator is by far
   the simplest way to intercept incoming values in all cases for the purposes
   of validation and simple marshaling.  See :ref:`simple_validators`
   for an example of this.

   For the second use case, the :ref:`associationproxy_toplevel` 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 :class:`~sqlalchemy.orm.session.Session` (i.e. the ``save-update``
cascade), as well as synchronizing the state of a bi-directional relationship
(i.e. a :func:`.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:

.. sourcecode:: python+sql

    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.

.. sourcecode:: python

    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:

.. sourcecode:: python+sql

    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.

.. _dictionary_collections:

Custom Dictionary-Based Collections
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The :class:`.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__``:

.. sourcecode:: python+sql

    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 :class:`.KeyFuncDict`, user-defined versions
of ``__setitem__()`` or ``__delitem__()`` should be decorated
with :meth:`.collection.internally_instrumented`, **if** they call down
to those same methods on :class:`.KeyFuncDict`.  This because the methods
on :class:`.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:

.. sourcecode:: python+sql

    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
-----------------------------

.. currentmodule:: sqlalchemy.orm

.. autofunction:: attribute_keyed_dict

.. autofunction:: column_keyed_dict

.. autofunction:: keyfunc_mapping

.. autodata:: attribute_mapped_collection

.. autodata:: column_mapped_collection

.. autodata:: mapped_collection

.. autoclass:: sqlalchemy.orm.KeyFuncDict
   :members:

.. autodata:: sqlalchemy.orm.MappedCollection


Collection Internals
-----------------------------

.. currentmodule:: sqlalchemy.orm.collections

.. autofunction:: bulk_replace

.. autoclass:: collection
    :members:

.. autodata:: collection_adapter

.. autoclass:: CollectionAdapter

.. autoclass:: InstrumentedDict

.. autoclass:: InstrumentedList

.. autoclass:: InstrumentedSet

.. autofunction:: prepare_instrumentation
