Collection Configuration and Techniques — SQLAlchemy 0.7.1 Documentation

# SQLAlchemy 0.7.1 Documentation

Search:
Version: 0.7.1 Last Updated: 06/05/2011 13:13:59

# Collection Configuration and Techniques

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.

## Working with Large Collections

The default behavior of `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

A key feature to enable management of a large collection is the so-called “dynamic” relationship. This is an optional form of `relationship()` which returns a `Query` object in place of a collection when accessed. `filter()` criterion may be applied as well as limits and offsets, either explicitly or via array slices:

    mapper(User, users_table, properties={
        'posts': relationship(Post, lazy="dynamic")
    })

    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 `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 the `backref()` function in conjunction with `lazy='dynamic'`:

    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.

Note

The `dynamic_loader()` function is essentially the same as `relationship()` with the `lazy='dynamic'` argument specified.

### Setting Noload

The opposite of the dynamic relationship is simply “noload”, specified using `lazy='noload'`:

    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:

    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.

## 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 `relationship()`.

    # 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 `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(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.

    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:

    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 `sqlalchemy.orm.collections` package provides several built-in types for dictionary-based collections:

    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 `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__`:

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

    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 `sqlalchemy.orm.collections` package for more information and discussion of advanced usage and Python 2.3-compatible decoration options.

### Collections API

`sqlalchemy.orm.collections.` `attribute_mapped_collection`(attr_name)

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

  Returns a MappedCollection factory with a keying based on the ‘attr_name’ attribute of entities in the collection, where `attr_name` is the string name of the attribute.

  The key value must be immutable for the lifetime of the object. You can not, for example, map on foreign key values if those key values will change during the session, i.e. from None to a database-assigned integer after a session flush.

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, internally_instrumented, link) 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:

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

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

  Decorators can be specified in long-hand for Python 2.3, or with the class-level dict attribute ‘__instrumentation__’- see the source for details.

  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): ...

  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.

  static `converter`(fn)

    Tag the method as the collection converter.

    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, it’s 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.

  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): ...

  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): ...

  static `link`(fn)

    Tag the method as a the “linked to attribute” event handler.

    This optional event handler will be called when the collection class is linked to or unlinked from the InstrumentedAttribute. It is invoked immediately after the ‘_sa_adapter’ property is set on the instance. A single argument is passed: the collection adapter that has been linked, or None if unlinking.

  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.

  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.

  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.

  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`(collection)

  Fetch the `CollectionAdapter` for a collection.

`sqlalchemy.orm.collections.` `column_mapped_collection`(mapping_spec)

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

  Returns a MappedCollection factory with a keying function generated from mapping_spec, which may be a Column or a sequence of Columns.

  The key value must be immutable for the lifetime of the object. You can not, for example, map on foreign key values if those key values will change during the session, i.e. from None to a database-assigned integer after a session flush.

`sqlalchemy.orm.collections.` `mapped_collection`(keyfunc)

  A dictionary-based collection type with arbitrary keying.

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

  The key value must be immutable for the lifetime of the object. You can not, for example, map on foreign key values if those key values will change during the session, i.e. from None to a database-assigned integer after a session flush.

class `sqlalchemy.orm.collections.` `MappedCollection`(keyfunc)

  A basic dictionary-based collection class.

  Extends dict with the minimal bag semantics that collection classes require. `set` and `remove` are implemented in terms of a keying function: any callable that takes an object and returns an object for use as a dictionary key.

  `__init__`(keyfunc)

    Create a new collection with keying provided by keyfunc.

    keyfunc may be any callable 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.

  `remove`(value, _sa_initiator=None)

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

  `set`(value, _sa_initiator=None)

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