==============
Making queries
==============

.. currentmodule:: django.db.models

Once you've created your :doc:`data models </topics/db/models>`, Django
automatically gives you a database-abstraction API that lets you create,
retrieve, update and delete objects. This document explains how to use this
API. Refer to the :doc:`data model reference </ref/models/index>` for full
details of all the various model lookup options.

Throughout this guide (and in the reference), we'll refer to the following
models, which comprise a Weblog application:

.. _queryset-model-example:

.. code-block:: python

    class Blog(models.Model):
        name = models.CharField(max_length=100)
        tagline = models.TextField()

        def __unicode__(self):
            return self.name

    class Author(models.Model):
        name = models.CharField(max_length=50)
        email = models.EmailField()

        def __unicode__(self):
            return self.name

    class Entry(models.Model):
        blog = models.ForeignKey(Blog)
        headline = models.CharField(max_length=255)
        body_text = models.TextField()
        pub_date = models.DateTimeField()
        authors = models.ManyToManyField(Author)
        n_comments = models.IntegerField()
        n_pingbacks = models.IntegerField()
        rating = models.IntegerField()

        def __unicode__(self):
            return self.headline

Creating objects
================

To represent database-table data in Python objects, Django uses an intuitive
system: A model class represents a database table, and an instance of that
class represents a particular record in the database table.

To create an object, instantiate it using keyword arguments to the model class,
then call ``save()`` to save it to the database.

You import the model class from wherever it lives on the Python path, as you
may expect. (We point this out here because previous Django versions required
funky model importing.)

Assuming models live in a file ``mysite/blog/models.py``, here's an example::

    >>> from mysite.blog.models import Blog
    >>> b = Blog(name='Beatles Blog', tagline='All the latest Beatles news.')
    >>> b.save()

This performs an ``INSERT`` SQL statement behind the scenes. Django doesn't hit
the database until you explicitly call ``save()``.

The ``save()`` method has no return value.

.. seealso::

    ``save()`` takes a number of advanced options not described here.
    See the documentation for ``save()`` for complete details.

    To create an object and save it all in one step see the ```create()```
    method.

Saving changes to objects
=========================

To save changes to an object that's already in the database, use ``save()``.

Given a ``Blog`` instance ``b5`` that has already been saved to the database,
this example changes its name and updates its record in the database::

    >> b5.name = 'New name'
    >> b5.save()

This performs an ``UPDATE`` SQL statement behind the scenes. Django doesn't hit
the database until you explicitly call ``save()``.

Saving ``ForeignKey`` and ``ManyToManyField`` fields
----------------------------------------------------

Updating a ``ForeignKey`` field works exactly the same way as saving a normal
field; simply assign an object of the right type to the field in question.
This example updates the ``blog`` attribute of an ``Entry`` instance ``entry``::

    >>> from mysite.blog.models import Entry
    >>> entry = Entry.objects.get(pk=1)
    >>> cheese_blog = Blog.objects.get(name="Cheddar Talk")
    >>> entry.blog = cheese_blog
    >>> entry.save()

Updating a ``ManyToManyField`` works a little differently; use the ``add()``
method on the field to add a record to the relation. This example adds the
``Author`` instance ``joe`` to the ``entry`` object::

    >>> from mysite.blog.models import Author
    >>> joe = Author.objects.create(name="Joe")
    >>> entry.authors.add(joe)

Django will complain if you try to assign or add an object of the wrong type.

Retrieving objects
==================

To retrieve objects from your database, you construct a ``QuerySet`` via a
``Manager`` on your model class.

A ``QuerySet`` represents a collection of objects from your database. It can
have zero, one or many *filters* -- criteria that narrow down the collection
based on given parameters. In SQL terms, a ``QuerySet`` equates to a ``SELECT``
statement, and a filter is a limiting clause such as ``WHERE`` or ``LIMIT``.

You get a ``QuerySet`` by using your model's ``Manager``. Each model has at
least one ``Manager``, and it's called ``objects`` by default. Access it
directly via the model class, like so::

    >>> Blog.objects
    <django.db.models.manager.Manager object at ...>
    >>> b = Blog(name='Foo', tagline='Bar')
    >>> b.objects
    Traceback:
        ...
    AttributeError: "Manager isn't accessible via Blog instances."

.. note::

    ``Managers`` are accessible only via model classes, rather than from model
    instances, to enforce a separation between "table-level" operations and
    "record-level" operations.

The ``Manager`` is the main source of ``QuerySets`` for a model. It acts as a
"root" ``QuerySet`` that describes all objects in the model's database table.
For example, ``Blog.objects`` is the initial ``QuerySet`` that contains all
``Blog`` objects in the database.

Retrieving all objects
----------------------

The simplest way to retrieve objects from a table is to get all of them.
To do this, use the ``all()`` method on a ``Manager``::

    >>> all_entries = Entry.objects.all()

The ``all()`` method returns a ``QuerySet`` of all the objects in the database.

(If ``Entry.objects`` is a ``QuerySet``, why can't we just do ``Entry.objects``?
That's because ``Entry.objects``, the root ``QuerySet``, is a special case
that cannot be evaluated. The ``all()`` method returns a ``QuerySet`` that
*can* be evaluated.)

Retrieving specific objects with filters
----------------------------------------

The root ``QuerySet`` provided by the ``Manager`` describes all objects in the
database table. Usually, though, you'll need to select only a subset of the
complete set of objects.

To create such a subset, you refine the initial ``QuerySet``, adding filter
conditions. The two most common ways to refine a ``QuerySet`` are:

    ``filter(**kwargs)``
        Returns a new ``QuerySet`` containing objects that match the given
        lookup parameters.

    ``exclude(**kwargs)``
        Returns a new ``QuerySet`` containing objects that do *not* match the
        given lookup parameters.

The lookup parameters (``**kwargs`` in the above function definitions) should
be in the format described in `Field lookups`_ below.

For example, to get a ``QuerySet`` of blog entries from the year 2006, use
``filter()`` like so::

    Entry.objects.filter(pub_date__year=2006)

We don't have to add an ``all()`` -- ``Entry.objects.all().filter(...)``. That
would still work, but you only need ``all()`` when you want all objects from the
root ``QuerySet``.

.. _chaining-filters:

Chaining filters
~~~~~~~~~~~~~~~~

The result of refining a ``QuerySet`` is itself a ``QuerySet``, so it's
possible to chain refinements together. For example::

    >>> Entry.objects.filter(
    ...     headline__startswith='What'
    ... ).exclude(
    ...     pub_date__gte=datetime.now()
    ... ).filter(
    ...     pub_date__gte=datetime(2005, 1, 1)
    ... )

This takes the initial ``QuerySet`` of all entries in the database, adds a
filter, then an exclusion, then another filter. The final result is a
``QuerySet`` containing all entries with a headline that starts with "What",
that were published between January 1, 2005, and the current day.

.. _filtered-querysets-are-unique:

Filtered QuerySets are unique
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Each time you refine a ``QuerySet``, you get a brand-new ``QuerySet`` that is
in no way bound to the previous ``QuerySet``. Each refinement creates a
separate and distinct ``QuerySet`` that can be stored, used and reused.

Example::

    >> q1 = Entry.objects.filter(headline__startswith="What")
    >> q2 = q1.exclude(pub_date__gte=datetime.now())
    >> q3 = q1.filter(pub_date__gte=datetime.now())

These three ``QuerySets`` are separate. The first is a base ``QuerySet``
containing all entries that contain a headline starting with "What". The second
is a subset of the first, with an additional criteria that excludes records
whose ``pub_date`` is greater than now. The third is a subset of the first,
with an additional criteria that selects only the records whose ``pub_date`` is
greater than now. The initial ``QuerySet`` (``q1``) is unaffected by the
refinement process.

.. _querysets-are-lazy:

QuerySets are lazy
~~~~~~~~~~~~~~~~~~

``QuerySets`` are lazy -- the act of creating a ``QuerySet`` doesn't involve any
database activity. You can stack filters together all day long, and Django won't
actually run the query until the ``QuerySet`` is *evaluated*. Take a look at
this example::

    >>> q = Entry.objects.filter(headline__startswith="What")
    >>> q = q.filter(pub_date__lte=datetime.now())
    >>> q = q.exclude(body_text__icontains="food")
    >>> print q

Though this looks like three database hits, in fact it hits the database only
once, at the last line (``print q``). In general, the results of a ``QuerySet``
aren't fetched from the database until you "ask" for them. When you do, the
``QuerySet`` is *evaluated* by accessing the database. For more details on
exactly when evaluation takes place, see :ref:`when-querysets-are-evaluated`.

Other QuerySet methods
~~~~~~~~~~~~~~~~~~~~~~

Most of the time you'll use ``all()``, ``filter()`` and ``exclude()`` when you
need to look up objects from the database. However, that's far from all there is; see the :ref:`QuerySet API Reference <queryset-api>` for a complete list
of all the various ``QuerySet`` methods.

.. _limiting-querysets:

Limiting QuerySets
------------------

Use a subset of Python's array-slicing syntax to limit your ``QuerySet`` to a
certain number of results. This is the equivalent of SQL's ``LIMIT`` and
``OFFSET`` clauses.

For example, this returns the first 5 objects (``LIMIT 5``)::

    >>> Entry.objects.all()[:5]

This returns the sixth through tenth objects (``OFFSET 5 LIMIT 5``)::

    >>> Entry.objects.all()[5:10]

Negative indexing (i.e. ``Entry.objects.all()[-1]``) is not supported.

Generally, slicing a ``QuerySet`` returns a new ``QuerySet`` -- it doesn't
evaluate the query. An exception is if you use the "step" parameter of Python
slice syntax. For example, this would actually execute the query in order to
return a list of every *second* object of the first 10::

    >>> Entry.objects.all()[:10:2]

To retrieve a *single* object rather than a list
(e.g. ``SELECT foo FROM bar LIMIT 1``), use a simple index instead of a
slice. For example, this returns the first ``Entry`` in the database, after
ordering entries alphabetically by headline::

    >>> Entry.objects.order_by('headline')[0]

This is roughly equivalent to::

    >>> Entry.objects.order_by('headline')[0:1].get()

Note, however, that the first of these will raise ``IndexError`` while the
second will raise ``DoesNotExist`` if no objects match the given criteria. See
``get()`` for more details.

.. _field-lookups-intro:

Field lookups
-------------

Field lookups are how you specify the meat of an SQL ``WHERE`` clause. They're
specified as keyword arguments to the ``QuerySet`` methods ``filter()``,
``exclude()`` and ``get()``.

Basic lookups keyword arguments take the form ``field__lookuptype=value``.
(That's a double-underscore). For example::

    >>> Entry.objects.filter(pub_date__lte='2006-01-01')

translates (roughly) into the following SQL::

    SELECT * FROM blog_entry WHERE pub_date <= '2006-01-01';

.. admonition:: How this is possible

   Python has the ability to define functions that accept arbitrary name-value
   arguments whose names and values are evaluated at runtime. For more
   information, see `Keyword Arguments`_ in the official Python tutorial.

   .. _`Keyword Arguments`: http://docs.python.org/tutorial/controlflow.html#keyword-arguments

If you pass an invalid keyword argument, a lookup function will raise
``TypeError``.

The database API supports about two dozen lookup types; a complete reference
can be found in the :ref:`field lookup reference <field-lookups>`. To give you a taste of what's available, here's some of the more common lookups
you'll probably use:

    :lookup:`exact`
        An "exact" match. For example::

            >>> Entry.objects.get(headline__exact="Man bites dog")

        Would generate SQL along these lines:

        .. code-block:: sql

            SELECT ... WHERE headline = 'Man bites dog';

        If you don't provide a lookup type -- that is, if your keyword argument
        doesn't contain a double underscore -- the lookup type is assumed to be
        ``exact``.

        For example, the following two statements are equivalent::

            >>> Blog.objects.get(id__exact=14)  # Explicit form
            >>> Blog.objects.get(id=14)         # __exact is implied

        This is for convenience, because ``exact`` lookups are the common case.

    :lookup:`iexact`
        A case-insensitive match. So, the query::

            >>> Blog.objects.get(name__iexact="beatles blog")

        Would match a ``Blog`` titled "Beatles Blog", "beatles blog", or even
        "BeAtlES blOG".

    :lookup:`contains`
        Case-sensitive containment test. For example::

            Entry.objects.get(headline__contains='Lennon')

        Roughly translates to this SQL:

        .. code-block:: sql

            SELECT ... WHERE headline LIKE '%Lennon%';

        Note this will match the headline ``'Today Lennon honored'`` but not
        ``'today lennon honored'``.

        There's also a case-insensitive version, :lookup:`icontains`.

    :lookup:`startswith`, :lookup:`endswith`
        Starts-with and ends-with search, respectively. There are also
        case-insensitive versions called :lookup:`istartswith` and
        :lookup:`iendswith`.

Again, this only scratches the surface. A complete reference can be found in the
:ref:`field lookup reference <field-lookups>`.

Lookups that span relationships
-------------------------------

Django offers a powerful and intuitive way to "follow" relationships in
lookups, taking care of the SQL ``JOIN``\s for you automatically, behind the
scenes. To span a relationship, just use the field name of related fields
across models, separated by double underscores, until you get to the field you
want.

This example retrieves all ``Entry`` objects with a ``Blog`` whose ``name``
is ``'Beatles Blog'``::

    >>> Entry.objects.filter(blog__name__exact='Beatles Blog')

This spanning can be as deep as you'd like.

It works backwards, too. To refer to a "reverse" relationship, just use the
lowercase name of the model.

This example retrieves all ``Blog`` objects which have at least one ``Entry``
whose ``headline`` contains ``'Lennon'``::

    >>> Blog.objects.filter(entry__headline__contains='Lennon')

If you are filtering across multiple relationships and one of the intermediate
models doesn't have a value that meets the filter condition, Django will treat
it as if there is an empty (all values are ``NULL``), but valid, object there.
All this means is that no error will be raised. For example, in this filter::

    Blog.objects.filter(entry__authors__name='Lennon')

(if there was a related ``Author`` model), if there was no ``author``
associated with an entry, it would be treated as if there was also no ``name``
attached, rather than raising an error because of the missing ``author``.
Usually this is exactly what you want to have happen. The only case where it
might be confusing is if you are using ``isnull``. Thus::

    Blog.objects.filter(entry__authors__name__isnull=True)

will return ``Blog`` objects that have an empty ``name`` on the ``author`` and
also those which have an empty ``author`` on the ``entry``. If you don't want
those latter objects, you could write::

    Blog.objects.filter(entry__authors__isnull=False,
            entry__authors__name__isnull=True)

Spanning multi-valued relationships
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. versionadded:: 1.0

When you are filtering an object based on a ``ManyToManyField`` or a reverse
``ForeignKey``, there are two different sorts of filter you may be
interested in. Consider the ``Blog``/``Entry`` relationship (``Blog`` to
``Entry`` is a one-to-many relation). We might be interested in finding blogs
that have an entry which has both *"Lennon"* in the headline and was published
in 2008. Or we might want to find blogs that have an entry with *"Lennon"* in
the headline as well as an entry that was published in 2008. Since there are
multiple entries associated with a single ``Blog``, both of these queries are
possible and make sense in some situations.

The same type of situation arises with a ``ManyToManyField``. For example, if
an ``Entry`` has a ``ManyToManyField`` called ``tags``, we might want to find
entries linked to tags called *"music"* and *"bands"* or we might want an
entry that contains a tag with a name of *"music"* and a status of *"public"*.

To handle both of these situations, Django has a consistent way of processing
``filter()`` and ``exclude()`` calls. Everything inside a single ``filter()``
call is applied simultaneously to filter out items matching all those
requirements. Successive ``filter()`` calls further restrict the set of
objects, but for multi-valued relations, they apply to any object linked to
the primary model, not necessarily those objects that were selected by an
earlier ``filter()`` call.

That may sound a bit confusing, so hopefully an example will clarify. To
select all blogs that contain entries with both *"Lennon"* in the headline
and that were published in 2008 (the same entry satisfying both conditions),
we would write::

    Blog.objects.filter(entry__headline__contains='Lennon',
            entry__pub_date__year=2008)

To select all blogs that contain an entry with *"Lennon"* in the headline
**as well as** an entry that was published in 2008, we would write::

    Blog.objects.filter(entry__headline__contains='Lennon').filter(
            entry__pub_date__year=2008)

In this second example, the first filter restricted the queryset to all those
blogs linked to that particular type of entry. The second filter restricted
the set of blogs *further* to those that are also linked to the second type of
entry. The entries select by the second filter may or may not be the same as
the entries in the first filter. We are filtering the ``Blog`` items with each
filter statement, not the ``Entry`` items.

All of this behavior also applies to ``exclude()``: all the conditions in a
single ``exclude()`` statement apply to a single instance (if those conditions
are talking about the same multi-valued relation). Conditions in subsequent
``filter()`` or ``exclude()`` calls that refer to the same relation may end up
filtering on different linked objects.

.. _query-expressions:

Filters can reference fields on the model
-----------------------------------------

.. versionadded:: 1.1

In the examples given so far, we have constructed filters that compare
the value of a model field with a constant. But what if you want to compare
the value of a model field with another field on the same model?

Django provides the ``F()`` object to allow such comparisons. Instances
of ``F()`` act as a reference to a model field within a query. These
references can then be used in query filters to compare the values of two
different fields on the same model instance.

For example, to find a list of all blog entries that have had more comments
than pingbacks, we construct an ``F()`` object to reference the comment count,
and use that ``F()`` object in the query::

    >>> from django.db.models import F
    >>> Entry.objects.filter(n_comments__gt=F('n_pingbacks'))

Django supports the use of addition, subtraction, multiplication,
division and modulo arithmetic with ``F()`` objects, both with constants
and with other ``F()`` objects. To find all the blog entries with more than
*twice* as many comments as pingbacks, we modify the query::

    >>> Entry.objects.filter(n_comments__gt=F('n_pingbacks') * 2)

To find all the entries where the rating of the entry is less than the
sum of the pingback count and comment count, we would issue the
query::

    >>> Entry.objects.filter(rating__lt=F('n_comments') + F('n_pingbacks'))

You can also use the double underscore notation to span relationships in
an ``F()`` object. An ``F()`` object with a double underscore will introduce
any joins needed to access the related object. For example, to retrieve all
the entries where the author's name is the same as the blog name, we could
issue the query:

    >>> Entry.objects.filter(authors__name=F('blog__name'))

The pk lookup shortcut
----------------------

For convenience, Django provides a ``pk`` lookup shortcut, which stands for
"primary key".

In the example ``Blog`` model, the primary key is the ``id`` field, so these
three statements are equivalent::

    >>> Blog.objects.get(id__exact=14) # Explicit form
    >>> Blog.objects.get(id=14) # __exact is implied
    >>> Blog.objects.get(pk=14) # pk implies id__exact

The use of ``pk`` isn't limited to ``__exact`` queries -- any query term
can be combined with ``pk`` to perform a query on the primary key of a model::

    # Get blogs entries with id 1, 4 and 7
    >>> Blog.objects.filter(pk__in=[1,4,7])

    # Get all blog entries with id > 14
    >>> Blog.objects.filter(pk__gt=14)

``pk`` lookups also work across joins. For example, these three statements are
equivalent::

    >>> Entry.objects.filter(blog__id__exact=3) # Explicit form
    >>> Entry.objects.filter(blog__id=3)        # __exact is implied
    >>> Entry.objects.filter(blog__pk=3)        # __pk implies __id__exact

Escaping percent signs and underscores in LIKE statements
---------------------------------------------------------

The field lookups that equate to ``LIKE`` SQL statements (``iexact``,
``contains``, ``icontains``, ``startswith``, ``istartswith``, ``endswith``
and ``iendswith``) will automatically escape the two special characters used in
``LIKE`` statements -- the percent sign and the underscore. (In a ``LIKE``
statement, the percent sign signifies a multiple-character wildcard and the
underscore signifies a single-character wildcard.)

This means things should work intuitively, so the abstraction doesn't leak.
For example, to retrieve all the entries that contain a percent sign, just use
the percent sign as any other character::

    >>> Entry.objects.filter(headline__contains='%')

Django takes care of the quoting for you; the resulting SQL will look something
like this:

.. code-block:: sql

    SELECT ... WHERE headline LIKE '%\%%';

Same goes for underscores. Both percentage signs and underscores are handled
for you transparently.

.. _caching-and-querysets:

Caching and QuerySets
---------------------

Each ``QuerySet`` contains a cache, to minimize database access. It's important
to understand how it works, in order to write the most efficient code.

In a newly created ``QuerySet``, the cache is empty. The first time a
``QuerySet`` is evaluated -- and, hence, a database query happens -- Django
saves the query results in the ``QuerySet``'s cache and returns the results
that have been explicitly requested (e.g., the next element, if the
``QuerySet`` is being iterated over). Subsequent evaluations of the
``QuerySet`` reuse the cached results.

Keep this caching behavior in mind, because it may bite you if you don't use
your ``QuerySet``\s correctly. For example, the following will create two
``QuerySet``\s, evaluate them, and throw them away::

    >>> print [e.headline for e in Entry.objects.all()]
    >>> print [e.pub_date for e in Entry.objects.all()]

That means the same database query will be executed twice, effectively doubling
your database load. Also, there's a possibility the two lists may not include
the same database records, because an ``Entry`` may have been added or deleted
in the split second between the two requests.

To avoid this problem, simply save the ``QuerySet`` and reuse it::

    >>> queryset = Entry.objects.all()
    >>> print [p.headline for p in queryset] # Evaluate the query set.
    >>> print [p.pub_date for p in queryset] # Re-use the cache from the evaluation.

.. _complex-lookups-with-q:

Complex lookups with Q objects
==============================

Keyword argument queries -- in ``filter()``, etc. -- are "AND"ed together. If
you need to execute more complex queries (for example, queries with ``OR``
statements), you can use ``Q`` objects.

A ``Q`` object (``django.db.models.Q``) is an object used to encapsulate a
collection of keyword arguments. These keyword arguments are specified as in
"Field lookups" above.

For example, this ``Q`` object encapsulates a single ``LIKE`` query::

    Q(question__startswith='What')

``Q`` objects can be combined using the ``&`` and ``|`` operators. When an
operator is used on two ``Q`` objects, it yields a new ``Q`` object.

For example, this statement yields a single ``Q`` object that represents the
"OR" of two ``"question__startswith"`` queries::

    Q(question__startswith='Who') | Q(question__startswith='What')

This is equivalent to the following SQL ``WHERE`` clause::

    WHERE question LIKE 'Who%' OR question LIKE 'What%'

You can compose statements of arbitrary complexity by combining ``Q`` objects
with the ``&`` and ``|`` operators and use parenthetical grouping. Also, ``Q``
objects can be negated using the ``~`` operator, allowing for combined lookups
that combine both a normal query and a negated (``NOT``) query::

    Q(question__startswith='Who') | ~Q(pub_date__year=2005)

Each lookup function that takes keyword-arguments (e.g. ``filter()``,
``exclude()``, ``get()``) can also be passed one or more ``Q`` objects as
positional (not-named) arguments. If you provide multiple ``Q`` object
arguments to a lookup function, the arguments will be "AND"ed together. For
example::

    Poll.objects.get(
        Q(question__startswith='Who'),
        Q(pub_date=date(2005, 5, 2)) | Q(pub_date=date(2005, 5, 6))
    )

... roughly translates into the SQL::

    SELECT * from polls WHERE question LIKE 'Who%'
        AND (pub_date = '2005-05-02' OR pub_date = '2005-05-06')

Lookup functions can mix the use of ``Q`` objects and keyword arguments. All
arguments provided to a lookup function (be they keyword arguments or ``Q``
objects) are "AND"ed together. However, if a ``Q`` object is provided, it must
precede the definition of any keyword arguments. For example::

    Poll.objects.get(
        Q(pub_date=date(2005, 5, 2)) | Q(pub_date=date(2005, 5, 6)),
        question__startswith='Who')

... would be a valid query, equivalent to the previous example; but::

    # INVALID QUERY
    Poll.objects.get(
        question__startswith='Who',
        Q(pub_date=date(2005, 5, 2)) | Q(pub_date=date(2005, 5, 6)))

... would not be valid.

.. seealso::

    The `OR lookups examples`_ in the Django unit tests show some possible uses
    of ``Q``.

    .. _OR lookups examples: http://code.djangoproject.com/browser/django/trunk/tests/modeltests/or_lookups/models.py

Comparing objects
=================

To compare two model instances, just use the standard Python comparison operator,
the double equals sign: ``==``. Behind the scenes, that compares the primary
key values of two models.

Using the ``Entry`` example above, the following two statements are equivalent::

    >>> some_entry == other_entry
    >>> some_entry.id == other_entry.id

If a model's primary key isn't called ``id``, no problem. Comparisons will
always use the primary key, whatever it's called. For example, if a model's
primary key field is called ``name``, these two statements are equivalent::

    >>> some_obj == other_obj
    >>> some_obj.name == other_obj.name

.. _topics-db-queries-delete:

Deleting objects
================

The delete method, conveniently, is named ``delete()``. This method immediately
deletes the object and has no return value. Example::

    e.delete()

You can also delete objects in bulk. Every ``QuerySet`` has a ``delete()``
method, which deletes all members of that ``QuerySet``.

For example, this deletes all ``Entry`` objects with a ``pub_date`` year of
2005::

    Entry.objects.filter(pub_date__year=2005).delete()

Keep in mind that this will, whenever possible, be executed purely in
SQL, and so the ``delete()`` methods of individual object instances
will not necessarily be called during the process. If you've provided
a custom ``delete()`` method on a model class and want to ensure that
it is called, you will need to "manually" delete instances of that
model (e.g., by iterating over a ``QuerySet`` and calling ``delete()``
on each object individually) rather than using the bulk ``delete()``
method of a ``QuerySet``.

When Django deletes an object, it emulates the behavior of the SQL
constraint ``ON DELETE CASCADE`` -- in other words, any objects which
had foreign keys pointing at the object to be deleted will be deleted
along with it. For example::

    b = Blog.objects.get(pk=1)
    # This will delete the Blog and all of its Entry objects.
    b.delete()

Note that ``delete()`` is the only ``QuerySet`` method that is not exposed on a
``Manager`` itself. This is a safety mechanism to prevent you from accidentally
requesting ``Entry.objects.delete()``, and deleting *all* the entries. If you
*do* want to delete all the objects, then you have to explicitly request a
complete query set::

    Entry.objects.all().delete()

.. _topics-db-queries-update:

Updating multiple objects at once
=================================

.. versionadded:: 1.0

Sometimes you want to set a field to a particular value for all the objects in
a ``QuerySet``. You can do this with the ``update()`` method. For example::

    # Update all the headlines with pub_date in 2007.
    Entry.objects.filter(pub_date__year=2007).update(headline='Everything is the same')

You can only set non-relation fields and ``ForeignKey`` fields using this
method. To update a non-relation field, provide the new value as a constant.
To update ``ForeignKey`` fields, set the new value to be the new model
instance you want to point to. For example::

    >>> b = Blog.objects.get(pk=1)

    # Change every Entry so that it belongs to this Blog.
    >>> Entry.objects.all().update(blog=b)

The ``update()`` method is applied instantly and returns the number of rows
affected by the query. The only restriction on the ``QuerySet`` that is
updated is that it can only access one database table, the model's main
table. You can filter based on related fields, but you can only update columns
in the model's main table. Example::

    >>> b = Blog.objects.get(pk=1)

    # Update all the headlines belonging to this Blog.
    >>> Entry.objects.select_related().filter(blog=b).update(headline='Everything is the same')

Be aware that the ``update()`` method is converted directly to an SQL
statement. It is a bulk operation for direct updates. It doesn't run any
``save()`` methods on your models, or emit the ``pre_save`` or ``post_save``
signals (which are a consequence of calling ``save()``). If you want to save
every item in a ``QuerySet`` and make sure that the ``save()`` method is
called on each instance, you don't need any special function to handle that.
Just loop over them and call ``save()``::

    for item in my_queryset:
        item.save()

.. versionadded:: 1.1

Calls to update can also use :ref:`F() objects <query-expressions>` to update
one field based on the value of another field in the model. This is especially
useful for incrementing counters based upon their current value. For example, to
increment the pingback count for every entry in the blog::

    >>> Entry.objects.all().update(n_pingbacks=F('n_pingbacks') + 1)

However, unlike ``F()`` objects in filter and exclude clauses, you can't
introduce joins when you use ``F()`` objects in an update -- you can only
reference fields local to the model being updated. If you attempt to introduce
a join with an ``F()`` object, a ``FieldError`` will be raised::

    # THIS WILL RAISE A FieldError
    >>> Entry.objects.update(headline=F('blog__name'))

.. _topics-db-queries-related:

Related objects
===============

When you define a relationship in a model (i.e., a ``ForeignKey``,
``OneToOneField``, or ``ManyToManyField``), instances of that model will have
a convenient API to access the related object(s).

Using the models at the top of this page, for example, an ``Entry`` object ``e``
can get its associated ``Blog`` object by accessing the ``blog`` attribute:
``e.blog``.

(Behind the scenes, this functionality is implemented by Python descriptors_.
This shouldn't really matter to you, but we point it out here for the curious.)

Django also creates API accessors for the "other" side of the relationship --
the link from the related model to the model that defines the relationship.
For example, a ``Blog`` object ``b`` has access to a list of all related
``Entry`` objects via the ``entry_set`` attribute: ``b.entry_set.all()``.

All examples in this section use the sample ``Blog``, ``Author`` and ``Entry``
models defined at the top of this page.

.. _descriptors: http://users.rcn.com/python/download/Descriptor.htm

One-to-many relationships
-------------------------

Forward
~~~~~~~

If a model has a ``ForeignKey``, instances of that model will have access to
the related (foreign) object via a simple attribute of the model.

Example::

    >>> e = Entry.objects.get(id=2)
    >>> e.blog # Returns the related Blog object.

You can get and set via a foreign-key attribute. As you may expect, changes to
the foreign key aren't saved to the database until you call ``save()``.
Example::

    >>> e = Entry.objects.get(id=2)
    >>> e.blog = some_blog
    >>> e.save()

If a ``ForeignKey`` field has ``null=True`` set (i.e., it allows ``NULL``
values), you can assign ``None`` to it. Example::

    >>> e = Entry.objects.get(id=2)
    >>> e.blog = None
    >>> e.save() # "UPDATE blog_entry SET blog_id = NULL ...;"

Forward access to one-to-many relationships is cached the first time the
related object is accessed. Subsequent accesses to the foreign key on the same
object instance are cached. Example::

    >>> e = Entry.objects.get(id=2)
    >>> print e.blog  # Hits the database to retrieve the associated Blog.
    >>> print e.blog  # Doesn't hit the database; uses cached version.

Note that the ``select_related()`` ``QuerySet`` method recursively prepopulates
the cache of all one-to-many relationships ahead of time. Example::

    >>> e = Entry.objects.select_related().get(id=2)
    >>> print e.blog  # Doesn't hit the database; uses cached version.
    >>> print e.blog  # Doesn't hit the database; uses cached version.

.. _backwards-related-objects:

Following relationships "backward"
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

If a model has a ``ForeignKey``, instances of the foreign-key model will have
access to a ``Manager`` that returns all instances of the first model. By
default, this ``Manager`` is named ``FOO_set``, where ``FOO`` is the source
model name, lowercased. This ``Manager`` returns ``QuerySets``, which can be
filtered and manipulated as described in the "Retrieving objects" section
above.

Example::

    >>> b = Blog.objects.get(id=1)
    >>> b.entry_set.all() # Returns all Entry objects related to Blog.

    # b.entry_set is a Manager that returns QuerySets.
    >>> b.entry_set.filter(headline__contains='Lennon')
    >>> b.entry_set.count()

You can override the ``FOO_set`` name by setting the ``related_name``
parameter in the ``ForeignKey()`` definition. For example, if the ``Entry``
model was altered to ``blog = ForeignKey(Blog, related_name='entries')``, the
above example code would look like this::

    >>> b = Blog.objects.get(id=1)
    >>> b.entries.all() # Returns all Entry objects related to Blog.

    # b.entries is a Manager that returns QuerySets.
    >>> b.entries.filter(headline__contains='Lennon')
    >>> b.entries.count()

You cannot access a reverse ``ForeignKey`` ``Manager`` from the class; it must
be accessed from an instance::

    >>> Blog.entry_set
    Traceback:
        ...
    AttributeError: "Manager must be accessed via instance".

In addition to the ``QuerySet`` methods defined in "Retrieving objects" above,
the ``ForeignKey`` ``Manager`` has additional methods used to handle the set of
related objects. A synopsis of each is below, and complete details can be found
in the :doc:`related objects reference </ref/models/relations>`.

``add(obj1, obj2, ...)``
    Adds the specified model objects to the related object set.

``create(**kwargs)``
    Creates a new object, saves it and puts it in the related object set.
    Returns the newly created object.

``remove(obj1, obj2, ...)``
    Removes the specified model objects from the related object set.

``clear()``
    Removes all objects from the related object set.

To assign the members of a related set in one fell swoop, just assign to it
from any iterable object. The iterable can contain object instances, or just
a list of primary key values. For example::

    b = Blog.objects.get(id=1)
    b.entry_set = [e1, e2]

In this example, ``e1`` and ``e2`` can be full Entry instances, or integer
primary key values.

If the ``clear()`` method is available, any pre-existing objects will be
removed from the ``entry_set`` before all objects in the iterable (in this
case, a list) are added to the set. If the ``clear()`` method is *not*
available, all objects in the iterable will be added without removing any
existing elements.

Each "reverse" operation described in this section has an immediate effect on
the database. Every addition, creation and deletion is immediately and
automatically saved to the database.

Many-to-many relationships
--------------------------

Both ends of a many-to-many relationship get automatic API access to the other
end. The API works just as a "backward" one-to-many relationship, above.

The only difference is in the attribute naming: The model that defines the
``ManyToManyField`` uses the attribute name of that field itself, whereas the
"reverse" model uses the lowercased model name of the original model, plus
``'_set'`` (just like reverse one-to-many relationships).

An example makes this easier to understand::

    e = Entry.objects.get(id=3)
    e.authors.all() # Returns all Author objects for this Entry.
    e.authors.count()
    e.authors.filter(name__contains='John')

    a = Author.objects.get(id=5)
    a.entry_set.all() # Returns all Entry objects for this Author.

Like ``ForeignKey``, ``ManyToManyField`` can specify ``related_name``. In the
above example, if the ``ManyToManyField`` in ``Entry`` had specified
``related_name='entries'``, then each ``Author`` instance would have an
``entries`` attribute instead of ``entry_set``.

One-to-one relationships
------------------------

One-to-one relationships are very similar to many-to-one relationships. If you
define a :class:`~django.db.models.OneToOneField` on your model, instances of
that model will have access to the related object via a simple attribute of the
model.

For example::

    class EntryDetail(models.Model):
        entry = models.OneToOneField(Entry)
        details = models.TextField()

    ed = EntryDetail.objects.get(id=2)
    ed.entry # Returns the related Entry object.

The difference comes in "reverse" queries. The related model in a one-to-one
relationship also has access to a :class:`~django.db.models.Manager` object, but
that :class:`~django.db.models.Manager` represents a single object, rather than
a collection of objects::

    e = Entry.objects.get(id=2)
    e.entrydetail # returns the related EntryDetail object

If no object has been assigned to this relationship, Django will raise
a ``DoesNotExist`` exception.

Instances can be assigned to the reverse relationship in the same way as
you would assign the forward relationship::

    e.entrydetail = ed

How are the backward relationships possible?
--------------------------------------------

Other object-relational mappers require you to define relationships on both
sides. The Django developers believe this is a violation of the DRY (Don't
Repeat Yourself) principle, so Django only requires you to define the
relationship on one end.

But how is this possible, given that a model class doesn't know which other
model classes are related to it until those other model classes are loaded?

The answer lies in the :setting:`INSTALLED_APPS` setting. The first time any model is
loaded, Django iterates over every model in :setting:`INSTALLED_APPS` and creates the
backward relationships in memory as needed. Essentially, one of the functions
of :setting:`INSTALLED_APPS` is to tell Django the entire model domain.

Queries over related objects
----------------------------

Queries involving related objects follow the same rules as queries involving
normal value fields. When specifying the value for a query to match, you may
use either an object instance itself, or the primary key value for the object.

For example, if you have a Blog object ``b`` with ``id=5``, the following
three queries would be identical::

    Entry.objects.filter(blog=b) # Query using object instance
    Entry.objects.filter(blog=b.id) # Query using id from instance
    Entry.objects.filter(blog=5) # Query using id directly

Falling back to raw SQL
=======================

If you find yourself needing to write an SQL query that is too complex for
Django's database-mapper to handle, you can fall back on writing SQL by hand.
Django has a couple of options for writing raw SQL queries; see
:doc:`/topics/db/sql`.

Finally, it's important to note that the Django database layer is merely an
interface to your database. You can access your database via other tools,
programming languages or database frameworks; there's nothing Django-specific
about your database.
