.. _collections_toplevel: .. currentmodule:: sqlalchemy.orm Collection Configuration and Techniques ======================================= The :func:`.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. .. _largecollections: .. currentmodule:: sqlalchemy.orm Working with Large Collections ------------------------------- The default behavior of :func:`.relationship` is to fully load the collection of items in, as according to the loading strategy of the relationship. Additionally, the Session by default only knows how to delete objects which are actually present within the session. When a parent instance is marked for deletion and flushed, the Session loads its full list of child items in so that they may either be deleted as well, or have their foreign key value set to null; this is to avoid constraint violations. For large collections of child items, there are several strategies to bypass full loading of child items both at load time as well as deletion time. Dynamic Relationship Loaders ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The most useful by far is the :func:`~sqlalchemy.orm.dynamic_loader` relationship. This is a variant of :func:`~sqlalchemy.orm.relationship` which returns a :class:`~sqlalchemy.orm.query.Query` object in place of a collection when accessed. :func:`~sqlalchemy.orm.query.Query.filter` criterion may be applied as well as limits and offsets, either explicitly or via array slices: .. sourcecode:: python+sql mapper(User, users_table, properties={ 'posts': dynamic_loader(Post) }) jack = session.query(User).get(id) # filter Jack's blog posts posts = jack.posts.filter(Post.headline=='this is a post') # apply array slices posts = jack.posts[5:20] The dynamic relationship supports limited write operations, via the ``append()`` and ``remove()`` methods:: oldpost = jack.posts.filter(Post.headline=='old post').one() jack.posts.remove(oldpost) jack.posts.append(Post('new post')) Since the read side of the dynamic relationship always queries the database, changes to the underlying collection will not be visible until the data has been flushed. However, as long as "autoflush" is enabled on the :class:`.Session` in use, this will occur automatically each time the collection is about to emit a query. To place a dynamic relationship on a backref, use ``lazy='dynamic'``: .. sourcecode:: python+sql mapper(Post, posts_table, properties={ 'user': relationship(User, backref=backref('posts', lazy='dynamic')) }) Note that eager/lazy loading options cannot be used in conjunction dynamic relationships at this time. .. autofunction:: dynamic_loader Setting Noload ~~~~~~~~~~~~~~~ The opposite of the dynamic relationship is simply "noload", specified using ``lazy='noload'``: .. sourcecode:: python+sql mapper(MyClass, table, properties={ 'children': relationship(MyOtherClass, lazy='noload') }) Above, the ``children`` collection is fully writeable, and changes to it will be persisted to the database as well as locally available for reading at the time they are added. However when instances of ``MyClass`` are freshly loaded from the database, the ``children`` collection stays empty. Using Passive Deletes ~~~~~~~~~~~~~~~~~~~~~~ Use ``passive_deletes=True`` to disable child object loading on a DELETE operation, in conjunction with "ON DELETE (CASCADE|SET NULL)" on your database to automatically cascade deletes to child objects. Note that "ON DELETE" is not supported on SQLite, and requires ``InnoDB`` tables when using MySQL: .. sourcecode:: python+sql mytable = Table('mytable', meta, Column('id', Integer, primary_key=True), ) myothertable = Table('myothertable', meta, Column('id', Integer, primary_key=True), Column('parent_id', Integer), ForeignKeyConstraint(['parent_id'], ['mytable.id'], ondelete="CASCADE"), ) mapper(MyOtherClass, myothertable) mapper(MyClass, mytable, properties={ 'children': relationship(MyOtherClass, cascade="all, delete-orphan", passive_deletes=True) }) When ``passive_deletes`` is applied, the ``children`` relationship will not be loaded into memory when an instance of ``MyClass`` is marked for deletion. The ``cascade="all, delete-orphan"`` *will* take effect for instances of ``MyOtherClass`` which are currently present in the session; however for instances of ``MyOtherClass`` which are not loaded, SQLAlchemy assumes that "ON DELETE CASCADE" rules will ensure that those rows are deleted by the database and that no foreign key violation will occur. .. currentmodule:: sqlalchemy.orm.collections .. _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. By default, this collection is a ``list``:: mapper(Parent, properties={ 'children' : relationship(Child) }) parent = Parent() parent.children.append(Child()) print parent.children[0] Collections are not limited to lists. Sets, mutable sequences and almost any other Python object that can act as a container can be used in place of the default list, by specifying the ``collection_class`` option on :func:`~sqlalchemy.orm.relationship`. .. sourcecode:: python+sql # use a set mapper(Parent, properties={ 'children' : relationship(Child, collection_class=set) }) parent = Parent() child = Child() parent.children.add(child) assert child in parent.children Custom Collection Implementations ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ You can use your own types for collections as well. For most cases, simply inherit from ``list`` or ``set`` and add the custom behavior. 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 ``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(object): 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 list-like methods, 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(object): __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 list-like because of ``append``, but ``__emulates__`` forces it to set-like. ``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. This set-like class does not provide the expected ``add`` method, so we must supply an explicit mapping for the ORM via a decorator. 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 you simply would like to use a different method to get the job done. .. sourcecode:: python+sql from sqlalchemy.orm.collections import collection class SetLike(object): __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-, 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-Based Collections ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ A ``dict`` can be used as a collection, but a keying strategy is needed to map entities loaded by the ORM to key, value pairs. The :mod:`sqlalchemy.orm.collections` package provides several built-in types for dictionary-based collections: .. sourcecode:: python+sql from sqlalchemy.orm.collections import column_mapped_collection, attribute_mapped_collection, mapped_collection mapper(Item, items_table, properties={ # key by column 'notes': relationship(Note, collection_class=column_mapped_collection(notes_table.c.keyword)), # or named attribute 'notes2': relationship(Note, collection_class=attribute_mapped_collection('keyword')), # or any callable 'notes3': relationship(Note, collection_class=mapped_collection(lambda entity: entity.a + entity.b)) }) # ... item = Item() item.notes['color'] = Note('color', 'blue') print item.notes['color'] These functions each provide a ``dict`` subclass with decorated ``set`` and ``remove`` methods and the keying strategy of your choice. The :class:`sqlalchemy.orm.collections.MappedCollection` 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.util import OrderedDict from sqlalchemy.orm.collections import MappedCollection class NodeMap(OrderedDict, MappedCollection): """Holds 'Node' objects, keyed by the 'name' attribute with insert order maintained.""" def __init__(self, *args, **kw): MappedCollection.__init__(self, keyfunc=lambda node: node.name) OrderedDict.__init__(self, *args, **kw) When subclassing :class:`.MappedCollection`, 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:`.MappedCollection`. This because the methods on :class:`.MappedCollection` 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 MappedCollection,\ collection class MyMappedCollection(MappedCollection): """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(MyMappedCollection, self).__setitem__(key, value, _sa_initiator) @collection.internally_instrumented def __delitem__(self, key, _sa_initiator=None): # do something with key super(MyMappedCollection, 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 ``itervalues()`` 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. The collections package provides additional decorators and support for authoring custom types. See the :mod:`sqlalchemy.orm.collections` package for more information and discussion of advanced usage and Python 2.3-compatible decoration options. Collections API ~~~~~~~~~~~~~~~ .. autofunction:: attribute_mapped_collection .. autoclass:: collection :members: .. autofunction:: collection_adapter .. autofunction:: column_mapped_collection .. autofunction:: mapped_collection .. autoclass:: sqlalchemy.orm.collections.MappedCollection :members: