======================
QuerySet API reference
======================

.. currentmodule:: django.db.models.QuerySet

This document describes the details of the ``QuerySet`` API. It builds on the
material presented in the :doc:`model </topics/db/models>` and :doc:`database
query </topics/db/queries>` guides, so you'll probably want to read and
understand those documents before reading this one.

Throughout this reference we'll use the :ref:`example Weblog models
<queryset-model-example>` presented in the :doc:`database query guide
</topics/db/queries>`.

.. _when-querysets-are-evaluated:

When QuerySets are evaluated
============================

Internally, a ``QuerySet`` can be constructed, filtered, sliced, and generally
passed around without actually hitting the database. No database activity
actually occurs until you do something to evaluate the queryset.

You can evaluate a ``QuerySet`` in the following ways:

    * **Iteration.** A ``QuerySet`` is iterable, and it executes its database
      query the first time you iterate over it. For example, this will print
      the headline of all entries in the database::

          for e in Entry.objects.all():
              print e.headline

    * **Slicing.** As explained in :ref:`limiting-querysets`, a ``QuerySet`` can
      be sliced, using Python's array-slicing syntax. Usually slicing a
      ``QuerySet`` returns another (unevaluated) ``QuerySet``, but Django will
      execute the database query if you use the "step" parameter of slice
      syntax.

    * **Pickling/Caching.** See the following section for details of what
      is involved when `pickling QuerySets`_. The important thing for the
      purposes of this section is that the results are read from the database.

    * **repr().** A ``QuerySet`` is evaluated when you call ``repr()`` on it.
      This is for convenience in the Python interactive interpreter, so you can
      immediately see your results when using the API interactively.

    * **len().** A ``QuerySet`` is evaluated when you call ``len()`` on it.
      This, as you might expect, returns the length of the result list.

      Note: *Don't* use ``len()`` on ``QuerySet``\s if all you want to do is
      determine the number of records in the set. It's much more efficient to
      handle a count at the database level, using SQL's ``SELECT COUNT(*)``,
      and Django provides a ``count()`` method for precisely this reason. See
      ``count()`` below.

    * **list().** Force evaluation of a ``QuerySet`` by calling ``list()`` on
      it. For example::

          entry_list = list(Entry.objects.all())

      Be warned, though, that this could have a large memory overhead, because
      Django will load each element of the list into memory. In contrast,
      iterating over a ``QuerySet`` will take advantage of your database to
      load data and instantiate objects only as you need them.

    * **bool().** Testing a ``QuerySet`` in a boolean context, such as using
      ``bool()``, ``or``, ``and`` or an ``if`` statement, will cause the query
      to be executed. If there is at least one result, the ``QuerySet`` is
      ``True``, otherwise ``False``. For example::

          if Entry.objects.filter(headline="Test"):
             print "There is at least one Entry with the headline Test"

      Note: *Don't* use this if all you want to do is determine if at least one
      result exists, and don't need the actual objects. It's more efficient to
      use ``exists()`` (see below).

.. _pickling QuerySets:

Pickling QuerySets
------------------

If you pickle_ a ``QuerySet``, this will force all the results to be loaded
into memory prior to pickling. Pickling is usually used as a precursor to
caching and when the cached queryset is reloaded, you want the results to
already be present and ready for use (reading from the database can take some
time, defeating the purpose of caching). This means that when you unpickle a
``QuerySet``, it contains the results at the moment it was pickled, rather
than the results that are currently in the database.

If you only want to pickle the necessary information to recreate the
``QuerySet`` from the database at a later time, pickle the ``query`` attribute
of the ``QuerySet``. You can then recreate the original ``QuerySet`` (without
any results loaded) using some code like this::

    >>> import pickle
    >>> query = pickle.loads(s)     # Assuming 's' is the pickled string.
    >>> qs = MyModel.objects.all()
    >>> qs.query = query            # Restore the original 'query'.

The ``query`` attribute is an opaque object. It represents the internals of
the query construction and is not part of the public API. However, it is safe
(and fully supported) to pickle and unpickle the attribute's contents as
described here.

.. admonition:: You can't share pickles between versions

	Pickles of QuerySets are only valid for the version of Django that
	was used to generate them. If you generate a pickle using Django
	version N, there is no guarantee that pickle will be readable with
	Django version N+1. Pickles should not be used as part of a long-term
	archival strategy.

.. _pickle: http://docs.python.org/library/pickle.html

.. _queryset-api:

QuerySet API
============

Though you usually won't create one manually -- you'll go through a
:class:`Manager` -- here's the formal declaration of a ``QuerySet``:

.. class:: QuerySet([model=None])

Usually when you'll interact with a ``QuerySet`` you'll use it by :ref:`chaining
filters <chaining-filters>`. To make this work, most ``QuerySet`` methods return new querysets.

Methods that return new QuerySets
---------------------------------

Django provides a range of ``QuerySet`` refinement methods that modify either
the types of results returned by the ``QuerySet`` or the way its SQL query is
executed.

filter
~~~~~~

.. method:: filter(**kwargs)

Returns a new ``QuerySet`` containing objects that match the given lookup
parameters.

The lookup parameters (``**kwargs``) should be in the format described in
`Field lookups`_ below. Multiple parameters are joined via ``AND`` in the
underlying SQL statement.

exclude
~~~~~~~

.. method:: exclude(**kwargs)

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

The lookup parameters (``**kwargs``) should be in the format described in
`Field lookups`_ below. Multiple parameters are joined via ``AND`` in the
underlying SQL statement, and the whole thing is enclosed in a ``NOT()``.

This example excludes all entries whose ``pub_date`` is later than 2005-1-3
AND whose ``headline`` is "Hello"::

    Entry.objects.exclude(pub_date__gt=datetime.date(2005, 1, 3), headline='Hello')

In SQL terms, that evaluates to::

    SELECT ...
    WHERE NOT (pub_date > '2005-1-3' AND headline = 'Hello')

This example excludes all entries whose ``pub_date`` is later than 2005-1-3
OR whose headline is "Hello"::

    Entry.objects.exclude(pub_date__gt=datetime.date(2005, 1, 3)).exclude(headline='Hello')

In SQL terms, that evaluates to::

    SELECT ...
    WHERE NOT pub_date > '2005-1-3'
    AND NOT headline = 'Hello'

Note the second example is more restrictive.

annotate
~~~~~~~~

.. method:: annotate(*args, **kwargs)

.. versionadded:: 1.1

Annotates each object in the ``QuerySet`` with the provided list of
aggregate values (averages, sums, etc) that have been computed over
the objects that are related to the objects in the ``QuerySet``.
Each argument to ``annotate()`` is an annotation that will be added
to each object in the ``QuerySet`` that is returned.

The aggregation functions that are provided by Django are described
in `Aggregation Functions`_ below.

Annotations specified using keyword arguments will use the keyword as
the alias for the annotation. Anonymous arguments will have an alias
generated for them based upon the name of the aggregate function and
the model field that is being aggregated.

For example, if you were manipulating a list of blogs, you may want
to determine how many entries have been made in each blog::

    >>> q = Blog.objects.annotate(Count('entry'))
    # The name of the first blog
    >>> q[0].name
    'Blogasaurus'
    # The number of entries on the first blog
    >>> q[0].entry__count
    42

The ``Blog`` model doesn't define an ``entry__count`` attribute by itself,
but by using a keyword argument to specify the aggregate function, you can
control the name of the annotation::

    >>> q = Blog.objects.annotate(number_of_entries=Count('entry'))
    # The number of entries on the first blog, using the name provided
    >>> q[0].number_of_entries
    42

For an in-depth discussion of aggregation, see :doc:`the topic guide on
Aggregation </topics/db/aggregation>`.

order_by
~~~~~~~~

.. method:: order_by(*fields)

By default, results returned by a ``QuerySet`` are ordered by the ordering
tuple given by the ``ordering`` option in the model's ``Meta``. You can
override this on a per-``QuerySet`` basis by using the ``order_by`` method.

Example::

    Entry.objects.filter(pub_date__year=2005).order_by('-pub_date', 'headline')

The result above will be ordered by ``pub_date`` descending, then by
``headline`` ascending. The negative sign in front of ``"-pub_date"`` indicates
*descending* order. Ascending order is implied. To order randomly, use ``"?"``,
like so::

    Entry.objects.order_by('?')

Note: ``order_by('?')`` queries may be expensive and slow, depending on the
database backend you're using.

To order by a field in a different model, use the same syntax as when you are
querying across model relations. That is, the name of the field, followed by a
double underscore (``__``), followed by the name of the field in the new model,
and so on for as many models as you want to join. For example::

    Entry.objects.order_by('blog__name', 'headline')

If you try to order by a field that is a relation to another model, Django will
use the default ordering on the related model (or order by the related model's
primary key if there is no ``Meta.ordering`` specified. For example::

    Entry.objects.order_by('blog')

...is identical to::

    Entry.objects.order_by('blog__id')

...since the ``Blog`` model has no default ordering specified.

Be cautious when ordering by fields in related models if you are also using
``distinct()``. See the note in :meth:`distinct` for an explanation of how
related model ordering can change the expected results.

It is permissible to specify a multi-valued field to order the results by (for
example, a ``ManyToMany`` field). Normally this won't be a sensible thing to
do and it's really an advanced usage feature. However, if you know that your
queryset's filtering or available data implies that there will only be one
ordering piece of data for each of the main items you are selecting, the
ordering may well be exactly what you want to do. Use ordering on multi-valued
fields with care and make sure the results are what you expect.

.. versionadded:: 1.0

The syntax for ordering across related models has changed. See the `Django 0.96
documentation`_ for the old behaviour.

.. _Django 0.96 documentation: http://www.djangoproject.com/documentation/0.96/model-api/#floatfield

There's no way to specify whether ordering should be case sensitive. With
respect to case-sensitivity, Django will order results however your database
backend normally orders them.

If you don't want any ordering to be applied to a query, not even the default
ordering, call ``order_by()`` with no parameters.

.. versionadded:: 1.1

You can tell if a query is ordered or not by checking the
:attr:`QuerySet.ordered` attribute, which will be ``True`` if the
``QuerySet`` has been ordered in any way.

reverse
~~~~~~~

.. method:: reverse()

.. versionadded:: 1.0

Use the ``reverse()`` method to reverse the order in which a queryset's
elements are returned. Calling ``reverse()`` a second time restores the
ordering back to the normal direction.

To retrieve the ''last'' five items in a queryset, you could do this::

    my_queryset.reverse()[:5]

Note that this is not quite the same as slicing from the end of a sequence in
Python. The above example will return the last item first, then the
penultimate item and so on. If we had a Python sequence and looked at
``seq[-5:]``, we would see the fifth-last item first. Django doesn't support
that mode of access (slicing from the end), because it's not possible to do it
efficiently in SQL.

Also, note that ``reverse()`` should generally only be called on a
``QuerySet`` which has a defined ordering (e.g., when querying against
a model which defines a default ordering, or when using
``order_by()``). If no such ordering is defined for a given
``QuerySet``, calling ``reverse()`` on it has no real effect (the
ordering was undefined prior to calling ``reverse()``, and will remain
undefined afterward).

distinct
~~~~~~~~

.. method:: distinct()

Returns a new ``QuerySet`` that uses ``SELECT DISTINCT`` in its SQL query. This
eliminates duplicate rows from the query results.

By default, a ``QuerySet`` will not eliminate duplicate rows. In practice, this
is rarely a problem, because simple queries such as ``Blog.objects.all()``
don't introduce the possibility of duplicate result rows. However, if your
query spans multiple tables, it's possible to get duplicate results when a
``QuerySet`` is evaluated. That's when you'd use ``distinct()``.

.. note::
    Any fields used in an :meth:`order_by` call are included in the SQL
    ``SELECT`` columns. This can sometimes lead to unexpected results when
    used in conjunction with ``distinct()``. If you order by fields from a
    related model, those fields will be added to the selected columns and they
    may make otherwise duplicate rows appear to be distinct. Since the extra
    columns don't appear in the returned results (they are only there to
    support ordering), it sometimes looks like non-distinct results are being
    returned.

    Similarly, if you use a ``values()`` query to restrict the columns
    selected, the columns used in any ``order_by()`` (or default model
    ordering) will still be involved and may affect uniqueness of the results.

    The moral here is that if you are using ``distinct()`` be careful about
    ordering by related models. Similarly, when using ``distinct()`` and
    ``values()`` together, be careful when ordering by fields not in the
    ``values()`` call.

values
~~~~~~

.. method:: values(*fields)

Returns a ``ValuesQuerySet`` -- a ``QuerySet`` that returns dictionaries when
used as an iterable, rather than model-instance objects.

Each of those dictionaries represents an object, with the keys corresponding to
the attribute names of model objects.

This example compares the dictionaries of ``values()`` with the normal model
objects::

    # This list contains a Blog object.
    >>> Blog.objects.filter(name__startswith='Beatles')
    [<Blog: Beatles Blog>]

    # This list contains a dictionary.
    >>> Blog.objects.filter(name__startswith='Beatles').values()
    [{'id': 1, 'name': 'Beatles Blog', 'tagline': 'All the latest Beatles news.'}]

``values()`` takes optional positional arguments, ``*fields``, which specify
field names to which the ``SELECT`` should be limited. If you specify the
fields, each dictionary will contain only the field keys/values for the fields
you specify. If you don't specify the fields, each dictionary will contain a
key and value for every field in the database table.

Example::

    >>> Blog.objects.values()
    [{'id': 1, 'name': 'Beatles Blog', 'tagline': 'All the latest Beatles news.'}],
    >>> Blog.objects.values('id', 'name')
    [{'id': 1, 'name': 'Beatles Blog'}]

A couple of subtleties that are worth mentioning:

    * The ``values()`` method does not return anything for
      :class:`~django.db.models.ManyToManyField` attributes and will raise an
      error if you try to pass in this type of field to it.
    * If you have a field called ``foo`` that is a
      :class:`~django.db.models.ForeignKey`, the default ``values()`` call
      will return a dictionary key called ``foo_id``, since this is the name
      of the hidden model attribute that stores the actual value (the ``foo``
      attribute refers to the related model). When you are calling
      ``values()`` and passing in field names, you can pass in either ``foo``
      or ``foo_id`` and you will get back the same thing (the dictionary key
      will match the field name you passed in).

      For example::

        >>> Entry.objects.values()
        [{'blog_id': 1, 'headline': u'First Entry', ...}, ...]

        >>> Entry.objects.values('blog')
        [{'blog': 1}, ...]

        >>> Entry.objects.values('blog_id')
        [{'blog_id': 1}, ...]

    * When using ``values()`` together with ``distinct()``, be aware that
      ordering can affect the results. See the note in :meth:`distinct` for
      details.

    * If you use a ``values()`` clause after an ``extra()`` clause,
      any fields defined by a ``select`` argument in the ``extra()``
      must be explicitly included in the ``values()`` clause. However,
      if the ``extra()`` clause is used after the ``values()``, the
      fields added by the select will be included automatically.

.. versionadded:: 1.0

Previously, it was not possible to pass ``blog_id`` to ``values()`` in the above
example, only ``blog``.

A ``ValuesQuerySet`` is useful when you know you're only going to need values
from a small number of the available fields and you won't need the
functionality of a model instance object. It's more efficient to select only
the fields you need to use.

Finally, note a ``ValuesQuerySet`` is a subclass of ``QuerySet``, so it has all
methods of ``QuerySet``. You can call ``filter()`` on it, or ``order_by()``, or
whatever. Yes, that means these two calls are identical::

    Blog.objects.values().order_by('id')
    Blog.objects.order_by('id').values()

The people who made Django prefer to put all the SQL-affecting methods first,
followed (optionally) by any output-affecting methods (such as ``values()``),
but it doesn't really matter. This is your chance to really flaunt your
individualism.

values_list
~~~~~~~~~~~

.. method:: values_list(*fields)

.. versionadded:: 1.0

This is similar to ``values()`` except that instead of returning dictionaries,
it returns tuples when iterated over. Each tuple contains the value from the
respective field passed into the ``values_list()`` call -- so the first item is
the first field, etc. For example::

    >>> Entry.objects.values_list('id', 'headline')
    [(1, u'First entry'), ...]

If you only pass in a single field, you can also pass in the ``flat``
parameter. If ``True``, this will mean the returned results are single values,
rather than one-tuples. An example should make the difference clearer::

    >>> Entry.objects.values_list('id').order_by('id')
    [(1,), (2,), (3,), ...]

    >>> Entry.objects.values_list('id', flat=True).order_by('id')
    [1, 2, 3, ...]

It is an error to pass in ``flat`` when there is more than one field.

If you don't pass any values to ``values_list()``, it will return all the
fields in the model, in the order they were declared.

dates
~~~~~

.. method:: dates(field, kind, order='ASC')

Returns a ``DateQuerySet`` -- a ``QuerySet`` that evaluates to a list of
``datetime.datetime`` objects representing all available dates of a particular
kind within the contents of the ``QuerySet``.

``field`` should be the name of a ``DateField`` or ``DateTimeField`` of your
model.

``kind`` should be either ``"year"``, ``"month"`` or ``"day"``. Each
``datetime.datetime`` object in the result list is "truncated" to the given
``type``.

    * ``"year"`` returns a list of all distinct year values for the field.
    * ``"month"`` returns a list of all distinct year/month values for the field.
    * ``"day"`` returns a list of all distinct year/month/day values for the field.

``order``, which defaults to ``'ASC'``, should be either ``'ASC'`` or
``'DESC'``. This specifies how to order the results.

Examples::

    >>> Entry.objects.dates('pub_date', 'year')
    [datetime.datetime(2005, 1, 1)]
    >>> Entry.objects.dates('pub_date', 'month')
    [datetime.datetime(2005, 2, 1), datetime.datetime(2005, 3, 1)]
    >>> Entry.objects.dates('pub_date', 'day')
    [datetime.datetime(2005, 2, 20), datetime.datetime(2005, 3, 20)]
    >>> Entry.objects.dates('pub_date', 'day', order='DESC')
    [datetime.datetime(2005, 3, 20), datetime.datetime(2005, 2, 20)]
    >>> Entry.objects.filter(headline__contains='Lennon').dates('pub_date', 'day')
    [datetime.datetime(2005, 3, 20)]

none
~~~~

.. method:: none()

.. versionadded:: 1.0

Returns an ``EmptyQuerySet`` -- a ``QuerySet`` that always evaluates to
an empty list. This can be used in cases where you know that you should
return an empty result set and your caller is expecting a ``QuerySet``
object (instead of returning an empty list, for example.)

Examples::

    >>> Entry.objects.none()
    []

all
~~~

.. method:: all()

.. versionadded:: 1.0

Returns a *copy* of the current ``QuerySet`` (or ``QuerySet`` subclass you
pass in). This can be useful in some situations where you might want to pass
in either a model manager or a ``QuerySet`` and do further filtering on the
result. You can safely call ``all()`` on either object and then you'll
definitely have a ``QuerySet`` to work with.

.. _select-related:

select_related
~~~~~~~~~~~~~~

.. method:: select_related()

Returns a ``QuerySet`` that will automatically "follow" foreign-key
relationships, selecting that additional related-object data when it executes
its query. This is a performance booster which results in (sometimes much)
larger queries but means later use of foreign-key relationships won't require
database queries.

The following examples illustrate the difference between plain lookups and
``select_related()`` lookups. Here's standard lookup::

    # Hits the database.
    e = Entry.objects.get(id=5)

    # Hits the database again to get the related Blog object.
    b = e.blog

And here's ``select_related`` lookup::

    # Hits the database.
    e = Entry.objects.select_related().get(id=5)

    # Doesn't hit the database, because e.blog has been prepopulated
    # in the previous query.
    b = e.blog

``select_related()`` follows foreign keys as far as possible. If you have the
following models::

    class City(models.Model):
        # ...

    class Person(models.Model):
        # ...
        hometown = models.ForeignKey(City)

    class Book(models.Model):
        # ...
        author = models.ForeignKey(Person)

...then a call to ``Book.objects.select_related().get(id=4)`` will cache the
related ``Person`` *and* the related ``City``::

    b = Book.objects.select_related().get(id=4)
    p = b.author         # Doesn't hit the database.
    c = p.hometown       # Doesn't hit the database.

    b = Book.objects.get(id=4) # No select_related() in this example.
    p = b.author         # Hits the database.
    c = p.hometown       # Hits the database.

Note that, by default, ``select_related()`` does not follow foreign keys that
have ``null=True``.

Usually, using ``select_related()`` can vastly improve performance because your
app can avoid many database calls. However, in situations with deeply nested
sets of relationships ``select_related()`` can sometimes end up following "too
many" relations, and can generate queries so large that they end up being slow.

In these situations, you can use the ``depth`` argument to ``select_related()``
to control how many "levels" of relations ``select_related()`` will actually
follow::

    b = Book.objects.select_related(depth=1).get(id=4)
    p = b.author         # Doesn't hit the database.
    c = p.hometown       # Requires a database call.

Sometimes you only want to access specific models that are related to your root
model, not all of the related models. In these cases, you can pass the related
field names to ``select_related()`` and it will only follow those relations.
You can even do this for models that are more than one relation away by
separating the field names with double underscores, just as for filters. For
example, if you have this model::

    class Room(models.Model):
        # ...
        building = models.ForeignKey(...)

    class Group(models.Model):
        # ...
        teacher = models.ForeignKey(...)
        room = models.ForeignKey(Room)
        subject = models.ForeignKey(...)

...and you only needed to work with the ``room`` and ``subject`` attributes,
you could write this::

    g = Group.objects.select_related('room', 'subject')

This is also valid::

    g = Group.objects.select_related('room__building', 'subject')

...and would also pull in the ``building`` relation.

You can refer to any ``ForeignKey`` or ``OneToOneField`` relation in
the list of fields passed to ``select_related``. Ths includes foreign
keys that have ``null=True`` (unlike the default ``select_related()``
call). It's an error to use both a list of fields and the ``depth``
parameter in the same ``select_related()`` call, since they are
conflicting options.

.. versionadded:: 1.0

Both the ``depth`` argument and the ability to specify field names in the call
to ``select_related()`` are new in Django version 1.0.

.. versionchanged:: 1.2

You can also refer to the reverse direction of a ``OneToOneFields`` in
the list of fields passed to ``select_related`` -- that is, you can traverse
a ``OneToOneField`` back to the object on which the field is defined. Instead
of specifying the field name, use the ``related_name`` for the field on the
related object.

``OneToOneFields`` will not be traversed in the reverse direction if you
are performing a depth-based ``select_related``.

extra
~~~~~

.. method:: extra(select=None, where=None, params=None, tables=None, order_by=None, select_params=None)

Sometimes, the Django query syntax by itself can't easily express a complex
``WHERE`` clause. For these edge cases, Django provides the ``extra()``
``QuerySet`` modifier -- a hook for injecting specific clauses into the SQL
generated by a ``QuerySet``.

By definition, these extra lookups may not be portable to different database
engines (because you're explicitly writing SQL code) and violate the DRY
principle, so you should avoid them if possible.

Specify one or more of ``params``, ``select``, ``where`` or ``tables``. None
of the arguments is required, but you should use at least one of them.

    * ``select``
        The ``select`` argument lets you put extra fields in the ``SELECT`` clause.
        It should be a dictionary mapping attribute names to SQL clauses to use to
        calculate that attribute.

        Example::

            Entry.objects.extra(select={'is_recent': "pub_date > '2006-01-01'"})

        As a result, each ``Entry`` object will have an extra attribute,
        ``is_recent``, a boolean representing whether the entry's ``pub_date`` is
        greater than Jan. 1, 2006.

        Django inserts the given SQL snippet directly into the ``SELECT``
        statement, so the resulting SQL of the above example would be something
        like::

            SELECT blog_entry.*, (pub_date > '2006-01-01') AS is_recent
            FROM blog_entry;


        The next example is more advanced; it does a subquery to give each
        resulting ``Blog`` object an ``entry_count`` attribute, an integer count
        of associated ``Entry`` objects::

            Blog.objects.extra(
                select={
                    'entry_count': 'SELECT COUNT(*) FROM blog_entry WHERE blog_entry.blog_id = blog_blog.id'
                },
            )

        (In this particular case, we're exploiting the fact that the query will
        already contain the ``blog_blog`` table in its ``FROM`` clause.)

        The resulting SQL of the above example would be::

            SELECT blog_blog.*, (SELECT COUNT(*) FROM blog_entry WHERE blog_entry.blog_id = blog_blog.id) AS entry_count
            FROM blog_blog;

        Note that the parenthesis required by most database engines around
        subqueries are not required in Django's ``select`` clauses. Also note that
        some database backends, such as some MySQL versions, don't support
        subqueries.

        .. versionadded:: 1.0

        In some rare cases, you might wish to pass parameters to the SQL fragments
        in ``extra(select=...)``. For this purpose, use the ``select_params``
        parameter. Since ``select_params`` is a sequence and the ``select``
        attribute is a dictionary, some care is required so that the parameters
        are matched up correctly with the extra select pieces.  In this situation,
        you should use a ``django.utils.datastructures.SortedDict`` for the
        ``select`` value, not just a normal Python dictionary.

        This will work, for example::

            Blog.objects.extra(
                select=SortedDict([('a', '%s'), ('b', '%s')]),
                select_params=('one', 'two'))

        The only thing to be careful about when using select parameters in
        ``extra()`` is to avoid using the substring ``"%%s"`` (that's *two*
        percent characters before the ``s``) in the select strings. Django's
        tracking of parameters looks for ``%s`` and an escaped ``%`` character
        like this isn't detected. That will lead to incorrect results.

    * ``where`` / ``tables``
        You can define explicit SQL ``WHERE`` clauses -- perhaps to perform
        non-explicit joins -- by using ``where``. You can manually add tables to
        the SQL ``FROM`` clause by using ``tables``.

        ``where`` and ``tables`` both take a list of strings. All ``where``
        parameters are "AND"ed to any other search criteria.

        Example::

            Entry.objects.extra(where=['id IN (3, 4, 5, 20)'])

        ...translates (roughly) into the following SQL::

            SELECT * FROM blog_entry WHERE id IN (3, 4, 5, 20);

        Be careful when using the ``tables`` parameter if you're specifying
        tables that are already used in the query. When you add extra tables
        via the ``tables`` parameter, Django assumes you want that table included
        an extra time, if it is already included. That creates a problem,
        since the table name will then be given an alias. If a table appears
        multiple times in an SQL statement, the second and subsequent occurrences
        must use aliases so the database can tell them apart. If you're
        referring to the extra table you added in the extra ``where`` parameter
        this is going to cause errors.

        Normally you'll only be adding extra tables that don't already appear in
        the query. However, if the case outlined above does occur, there are a few
        solutions. First, see if you can get by without including the extra table
        and use the one already in the query. If that isn't possible, put your
        ``extra()`` call at the front of the queryset construction so that your
        table is the first use of that table. Finally, if all else fails, look at
        the query produced and rewrite your ``where`` addition to use the alias
        given to your extra table. The alias will be the same each time you
        construct the queryset in the same way, so you can rely upon the alias
        name to not change.

    * ``order_by``
        If you need to order the resulting queryset using some of the new fields
        or tables you have included via ``extra()`` use the ``order_by`` parameter
        to ``extra()`` and pass in a sequence of strings. These strings should
        either be model fields (as in the normal ``order_by()`` method on
        querysets), of the form ``table_name.column_name`` or an alias for a column
        that you specified in the ``select`` parameter to ``extra()``.

        For example::

            q = Entry.objects.extra(select={'is_recent': "pub_date > '2006-01-01'"})
            q = q.extra(order_by = ['-is_recent'])

        This would sort all the items for which ``is_recent`` is true to the front
        of the result set (``True`` sorts before ``False`` in a descending
        ordering).

        This shows, by the way, that you can make multiple calls to
        ``extra()`` and it will behave as you expect (adding new constraints each
        time).

    * ``params``
        The ``where`` parameter described above may use standard Python database
        string placeholders -- ``'%s'`` to indicate parameters the database engine
        should automatically quote. The ``params`` argument is a list of any extra
        parameters to be substituted.

        Example::

            Entry.objects.extra(where=['headline=%s'], params=['Lennon'])

        Always use ``params`` instead of embedding values directly into ``where``
        because ``params`` will ensure values are quoted correctly according to
        your particular backend. (For example, quotes will be escaped correctly.)

        Bad::

            Entry.objects.extra(where=["headline='Lennon'"])

        Good::

            Entry.objects.extra(where=['headline=%s'], params=['Lennon'])

defer
~~~~~

.. method:: defer(*fields)

.. versionadded:: 1.1

In some complex data-modeling situations, your models might contain a lot of
fields, some of which could contain a lot of data (for example, text fields),
or require expensive processing to convert them to Python objects. If you are
using the results of a queryset in some situation where you know you don't
need those particular fields, you can tell Django not to retrieve them from
the database.

This is done by passing the names of the fields to not load to ``defer()``::

    Entry.objects.defer("headline", "body")

A queryset that has deferred fields will still return model instances. Each
deferred field will be retrieved from the database if you access that field
(one at a time, not all the deferred fields at once).

You can make multiple calls to ``defer()``. Each call adds new fields to the
deferred set::

    # Defers both the body and headline fields.
    Entry.objects.defer("body").filter(rating=5).defer("headline")

The order in which fields are added to the deferred set does not matter.
Calling ``defer()`` with a field name that has already been deferred is
harmless (the field will still be deferred).

You can defer loading of fields in related models (if the related models are
loading via ``select_related()``) by using the standard double-underscore
notation to separate related fields::

    Blog.objects.select_related().defer("entry__headline", "entry__body")

If you want to clear the set of deferred fields, pass ``None`` as a parameter
to ``defer()``::

    # Load all fields immediately.
    my_queryset.defer(None)

Some fields in a model won't be deferred, even if you ask for them. You can
never defer the loading of the primary key. If you are using
``select_related()`` to retrieve other models at the same time you shouldn't
defer the loading of the field that connects from the primary model to the
related one (at the moment, that doesn't raise an error, but it will
eventually).

.. note::

    The ``defer()`` method (and its cousin, ``only()``, below) are only for
    advanced use-cases. They provide an optimization for when you have
    analyzed your queries closely and understand *exactly* what information
    you need and have measured that the difference between returning the
    fields you need and the full set of fields for the model will be
    significant. When you are initially developing your applications, don't
    bother using ``defer()``; leave it until your query construction has
    settled down and you understand where the hot-points are.

only
~~~~

.. method:: only(*fields)

.. versionadded:: 1.1

The ``only()`` method is more or less the opposite of ``defer()``. You
call it with the fields that should *not* be deferred when retrieving a model.
If you have a model where almost all the fields need to be deferred, using
``only()`` to specify the complementary set of fields could result in simpler
code.

If you have a model with fields ``name``, ``age`` and ``biography``, the
following two querysets are the same, in terms of deferred fields::

    Person.objects.defer("age", "biography")
    Person.objects.only("name")

Whenever you call ``only()`` it *replaces* the set of fields to load
immediately. The method's name is mnemonic: **only** those fields are loaded
immediately; the remainder are deferred. Thus, successive calls to ``only()``
result in only the final fields being considered::

    # This will defer all fields except the headline.
    Entry.objects.only("body", "rating").only("headline")

Since ``defer()`` acts incrementally (adding fields to the deferred list), you
can combine calls to ``only()`` and ``defer()`` and things will behave
logically::

    # Final result is that everything except "headline" is deferred.
    Entry.objects.only("headline", "body").defer("body")

    # Final result loads headline and body immediately (only() replaces any
    # existing set of fields).
    Entry.objects.defer("body").only("headline", "body")

using
~~~~~

.. method:: using(alias)

.. versionadded:: 1.2

This method is for controlling which database the ``QuerySet`` will be
evaluated against if you are using more than one database.  The only argument
this method takes is the alias of a database, as defined in
:setting:`DATABASES`.

For example::

    # queries the database with the 'default' alias.
    >>> Entry.objects.all()

    # queries the database with the 'backup' alias
    >>> Entry.objects.using('backup')


Methods that do not return QuerySets
------------------------------------

The following ``QuerySet`` methods evaluate the ``QuerySet`` and return
something *other than* a ``QuerySet``.

These methods do not use a cache (see :ref:`caching-and-querysets`). Rather,
they query the database each time they're called.

get
~~~

.. method:: get(**kwargs)

Returns the object matching the given lookup parameters, which should be in
the format described in `Field lookups`_.

``get()`` raises ``MultipleObjectsReturned`` if more than one object was
found. The ``MultipleObjectsReturned`` exception is an attribute of the model
class.

``get()`` raises a ``DoesNotExist`` exception if an object wasn't found for
the given parameters. This exception is also an attribute of the model class.
Example::

    Entry.objects.get(id='foo') # raises Entry.DoesNotExist

The ``DoesNotExist`` exception inherits from
``django.core.exceptions.ObjectDoesNotExist``, so you can target multiple
``DoesNotExist`` exceptions. Example::

    from django.core.exceptions import ObjectDoesNotExist
    try:
        e = Entry.objects.get(id=3)
        b = Blog.objects.get(id=1)
    except ObjectDoesNotExist:
        print "Either the entry or blog doesn't exist."

create
~~~~~~

.. method:: create(**kwargs)

A convenience method for creating an object and saving it all in one step.  Thus::

    p = Person.objects.create(first_name="Bruce", last_name="Springsteen")

and::

    p = Person(first_name="Bruce", last_name="Springsteen")
    p.save(force_insert=True)

are equivalent.

The :ref:`force_insert <ref-models-force-insert>` parameter is documented
elsewhere, but all it means is that a new object will always be created.
Normally you won't need to worry about this. However, if your model contains a
manual primary key value that you set and if that value already exists in the
database, a call to ``create()`` will fail with an :exc:`IntegrityError` since
primary keys must be unique. So remember to be prepared to handle the exception
if you are using manual primary keys.

get_or_create
~~~~~~~~~~~~~

.. method:: get_or_create(**kwargs)

A convenience method for looking up an object with the given kwargs, creating
one if necessary.

Returns a tuple of ``(object, created)``, where ``object`` is the retrieved or
created object and ``created`` is a boolean specifying whether a new object was
created.

This is meant as a shortcut to boilerplatish code and is mostly useful for
data-import scripts. For example::

    try:
        obj = Person.objects.get(first_name='John', last_name='Lennon')
    except Person.DoesNotExist:
        obj = Person(first_name='John', last_name='Lennon', birthday=date(1940, 10, 9))
        obj.save()

This pattern gets quite unwieldy as the number of fields in a model goes up.
The above example can be rewritten using ``get_or_create()`` like so::

    obj, created = Person.objects.get_or_create(first_name='John', last_name='Lennon',
                      defaults={'birthday': date(1940, 10, 9)})

Any keyword arguments passed to ``get_or_create()`` -- *except* an optional one
called ``defaults`` -- will be used in a ``get()`` call. If an object is found,
``get_or_create()`` returns a tuple of that object and ``False``. If an object
is *not* found, ``get_or_create()`` will instantiate and save a new object,
returning a tuple of the new object and ``True``. The new object will be
created roughly according to this algorithm::

    defaults = kwargs.pop('defaults', {})
    params = dict([(k, v) for k, v in kwargs.items() if '__' not in k])
    params.update(defaults)
    obj = self.model(**params)
    obj.save()

In English, that means start with any non-``'defaults'`` keyword argument that
doesn't contain a double underscore (which would indicate a non-exact lookup).
Then add the contents of ``defaults``, overriding any keys if necessary, and
use the result as the keyword arguments to the model class. As hinted at
above, this is a simplification of the algorithm that is used, but it contains
all the pertinent details. The internal implementation has some more
error-checking than this and handles some extra edge-conditions; if you're
interested, read the code.

If you have a field named ``defaults`` and want to use it as an exact lookup in
``get_or_create()``, just use ``'defaults__exact'``, like so::

    Foo.objects.get_or_create(defaults__exact='bar', defaults={'defaults': 'baz'})


The ``get_or_create()`` method has similar error behaviour to ``create()``
when you are using manually specified primary keys. If an object needs to be
created and the key already exists in the database, an ``IntegrityError`` will
be raised.

Finally, a word on using ``get_or_create()`` in Django views. As mentioned
earlier, ``get_or_create()`` is mostly useful in scripts that need to parse
data and create new records if existing ones aren't available. But if you need
to use ``get_or_create()`` in a view, please make sure to use it only in
``POST`` requests unless you have a good reason not to. ``GET`` requests
shouldn't have any effect on data; use ``POST`` whenever a request to a page
has a side effect on your data. For more, see `Safe methods`_ in the HTTP spec.

.. _Safe methods: http://www.w3.org/Protocols/rfc2616/rfc2616-sec9.html#sec9.1.1

count
~~~~~

.. method:: count()

Returns an integer representing the number of objects in the database matching
the ``QuerySet``. ``count()`` never raises exceptions.

Example::

    # Returns the total number of entries in the database.
    Entry.objects.count()

    # Returns the number of entries whose headline contains 'Lennon'
    Entry.objects.filter(headline__contains='Lennon').count()

``count()`` performs a ``SELECT COUNT(*)`` behind the scenes, so you should
always use ``count()`` rather than loading all of the record into Python
objects and calling ``len()`` on the result (unless you need to load the
objects into memory anyway, in which case ``len()`` will be faster).

Depending on which database you're using (e.g. PostgreSQL vs. MySQL),
``count()`` may return a long integer instead of a normal Python integer. This
is an underlying implementation quirk that shouldn't pose any real-world
problems.

in_bulk
~~~~~~~

.. method:: in_bulk(id_list)

Takes a list of primary-key values and returns a dictionary mapping each
primary-key value to an instance of the object with the given ID.

Example::

    >>> Blog.objects.in_bulk([1])
    {1: <Blog: Beatles Blog>}
    >>> Blog.objects.in_bulk([1, 2])
    {1: <Blog: Beatles Blog>, 2: <Blog: Cheddar Talk>}
    >>> Blog.objects.in_bulk([])
    {}

If you pass ``in_bulk()`` an empty list, you'll get an empty dictionary.

iterator
~~~~~~~~

.. method:: iterator()

Evaluates the ``QuerySet`` (by performing the query) and returns an
`iterator`_ over the results. A ``QuerySet`` typically caches its
results internally so that repeated evaluations do not result in
additional queries; ``iterator()`` will instead read results directly,
without doing any caching at the ``QuerySet`` level. For a
``QuerySet`` which returns a large number of objects, this often
results in better performance and a significant reduction in memory

Note that using ``iterator()`` on a ``QuerySet`` which has already
been evaluated will force it to evaluate again, repeating the query.

.. _iterator: http://www.python.org/dev/peps/pep-0234/

latest
~~~~~~

.. method:: latest(field_name=None)

Returns the latest object in the table, by date, using the ``field_name``
provided as the date field.

This example returns the latest ``Entry`` in the table, according to the
``pub_date`` field::

    Entry.objects.latest('pub_date')

If your model's ``Meta`` specifies ``get_latest_by``, you can leave off the
``field_name`` argument to ``latest()``. Django will use the field specified in
``get_latest_by`` by default.

Like ``get()``, ``latest()`` raises ``DoesNotExist`` if an object doesn't
exist with the given parameters.

Note ``latest()`` exists purely for convenience and readability.

aggregate
~~~~~~~~~

.. method:: aggregate(*args, **kwargs)

.. versionadded:: 1.1

Returns a dictionary of aggregate values (averages, sums, etc) calculated
over the ``QuerySet``. Each argument to ``aggregate()`` specifies
a value that will be included in the dictionary that is returned.

The aggregation functions that are provided by Django are described
in `Aggregation Functions`_ below.

Aggregates specified using keyword arguments will use the keyword as
the name for the annotation. Anonymous arguments will have an name
generated for them based upon the name of the aggregate function and
the model field that is being aggregated.

For example, if you were manipulating blog entries, you may want to know
the number of authors that have contributed blog entries::

    >>> q = Blog.objects.aggregate(Count('entry'))
    {'entry__count': 16}

By using a keyword argument to specify the aggregate function, you can
control the name of the aggregation value that is returned::

    >>> q = Blog.objects.aggregate(number_of_entries=Count('entry'))
    {'number_of_entries': 16}

For an in-depth discussion of aggregation, see :doc:`the topic guide on
Aggregation </topics/db/aggregation>`.

exists
~~~~~~

.. method:: exists()

.. versionadded:: 1.2

Returns ``True`` if the :class:`QuerySet` contains any results, and ``False``
if not. This tries to perform the query in the simplest and fastest way
possible, but it *does* execute nearly the same query. This means that calling
:meth:`QuerySet.exists()` is faster than ``bool(some_query_set)``, but not by
a large degree.  If ``some_query_set`` has not yet been evaluated, but you know
that it will be at some point, then using ``some_query_set.exists()`` will do
more overall work (an additional query) than simply using
``bool(some_query_set)``.

update
~~~~~~

.. method:: update(**kwargs)

Performs an SQL update query for the specified fields, and returns
the number of rows affected. The ``update()`` method is applied instantly and
the only restriction on the :class:`QuerySet` that is updated is that it can
only update columns in the model's main table. Filtering based on related
fields is still possible. You cannot call ``update()`` on a
:class:`QuerySet` that has had a slice taken or can otherwise no longer be
filtered.

For example, if you wanted to update all the entries in a particular blog
to use the same headline::

    >>> 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')

The ``update()`` method does a bulk update and does not call any ``save()``
methods on your models, nor does it emit the ``pre_save`` or ``post_save``
signals (which are a consequence of calling ``save()``).

delete
~~~~~~

.. method:: delete()

Performs an SQL delete query on all rows in the :class:`QuerySet`. The
``delete()`` is applied instantly. You cannot call ``delete()`` on a
:class:`QuerySet` that has had a slice taken or can otherwise no longer be
filtered.

For example, to delete all the entries in a particular blog::

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

    # Delete all the entries belonging to this Blog.
    >>> Entry.objects.filter(blog=b).delete()

Django emulates the SQL constraint ``ON DELETE CASCADE`` -- in other words, any
objects with foreign keys pointing at the objects to be deleted will be deleted
along with them.  For example::

    blogs = Blog.objects.all()
    # This will delete all Blogs and all of their Entry objects.
    blogs.delete()

The ``delete()`` method does a bulk delete and does not call any ``delete()``
methods on your models. It does, however, emit the
:data:`~django.db.models.signals.pre_delete` and
:data:`~django.db.models.signals.post_delete` signals for all deleted objects
(including cascaded deletions).

.. _field-lookups:

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()``.

For an introduction, see :ref:`field-lookups-intro`.

.. fieldlookup:: exact

exact
~~~~~

Exact match. If the value provided for comparison is ``None``, it will
be interpreted as an SQL ``NULL`` (See isnull_ for more details).

Examples::

    Entry.objects.get(id__exact=14)
    Entry.objects.get(id__exact=None)

SQL equivalents::

    SELECT ... WHERE id = 14;
    SELECT ... WHERE id IS NULL;

.. versionchanged:: 1.0
   The semantics of ``id__exact=None`` have changed in Django 1.0. Previously,
   it was (intentionally) converted to ``WHERE id = NULL`` at the SQL level,
   which would never match anything. It has now been changed to behave the
   same as ``id__isnull=True``.

.. admonition:: MySQL comparisons

    In MySQL, a database table's "collation" setting determines whether
    ``exact`` comparisons are case-sensitive. This is a database setting, *not*
    a Django setting. It's possible to configure your MySQL tables to use
    case-sensitive comparisons, but some trade-offs are involved. For more
    information about this, see the :ref:`collation section <mysql-collation>`
    in the :doc:`databases </ref/databases>` documentation.

.. fieldlookup:: iexact

iexact
~~~~~~

Case-insensitive exact match.

Example::

    Blog.objects.get(name__iexact='beatles blog')

SQL equivalent::

    SELECT ... WHERE name ILIKE 'beatles blog';

Note this will match ``'Beatles Blog'``, ``'beatles blog'``, ``'BeAtLes
BLoG'``, etc.

.. admonition:: SQLite users

    When using the SQLite backend and Unicode (non-ASCII) strings, bear in
    mind the :ref:`database note <sqlite-string-matching>` about string
    comparisons. SQLite does not do case-insensitive matching for Unicode
    strings.

.. fieldlookup:: contains

contains
~~~~~~~~

Case-sensitive containment test.

Example::

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

SQL equivalent::

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

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

SQLite doesn't support case-sensitive ``LIKE`` statements; ``contains`` acts
like ``icontains`` for SQLite.

.. fieldlookup:: icontains

icontains
~~~~~~~~~

Case-insensitive containment test.

Example::

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

SQL equivalent::

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

.. admonition:: SQLite users

    When using the SQLite backend and Unicode (non-ASCII) strings, bear in
    mind the :ref:`database note <sqlite-string-matching>` about string
    comparisons.

.. fieldlookup:: in

in
~~

In a given list.

Example::

    Entry.objects.filter(id__in=[1, 3, 4])

SQL equivalent::

    SELECT ... WHERE id IN (1, 3, 4);

You can also use a queryset to dynamically evaluate the list of values
instead of providing a list of literal values::

    inner_qs = Blog.objects.filter(name__contains='Cheddar')
    entries = Entry.objects.filter(blog__in=inner_qs)

This queryset will be evaluated as subselect statement::

    SELECT ... WHERE blog.id IN (SELECT id FROM ... WHERE NAME LIKE '%Cheddar%')

The above code fragment could also be written as follows::

    inner_q = Blog.objects.filter(name__contains='Cheddar').values('pk').query
    entries = Entry.objects.filter(blog__in=inner_q)


.. versionchanged:: 1.1
    In Django 1.0, only the latter piece of code is valid.

This second form is a bit less readable and unnatural to write, since it
accesses the internal ``query`` attribute and requires a ``ValuesQuerySet``.
If your code doesn't require compatibility with Django 1.0, use the first
form, passing in a queryset directly.

If you pass in a ``ValuesQuerySet`` or ``ValuesListQuerySet`` (the result of
calling ``values()`` or ``values_list()`` on a queryset) as the value to an
``__in`` lookup, you need to ensure you are only extracting one field in the
result. For example, this will work (filtering on the blog names)::

    inner_qs = Blog.objects.filter(name__contains='Ch').values('name')
    entries = Entry.objects.filter(blog__name__in=inner_qs)

This example will raise an exception, since the inner query is trying to
extract two field values, where only one is expected::

    # Bad code! Will raise a TypeError.
    inner_qs = Blog.objects.filter(name__contains='Ch').values('name', 'id')
    entries = Entry.objects.filter(blog__name__in=inner_qs)

.. warning::

    This ``query`` attribute should be considered an opaque internal attribute.
    It's fine to use it like above, but its API may change between Django
    versions.

.. admonition:: Performance considerations

    Be cautious about using nested queries and understand your database
    server's performance characteristics (if in doubt, benchmark!). Some
    database backends, most notably MySQL, don't optimize nested queries very
    well. It is more efficient, in those cases, to extract a list of values
    and then pass that into the second query. That is, execute two queries
    instead of one::

        values = Blog.objects.filter(
                name__contains='Cheddar').values_list('pk', flat=True)
        entries = Entry.objects.filter(blog__in=list(values))

    Note the ``list()`` call around the Blog ``QuerySet`` to force execution of
    the first query. Without it, a nested query would be executed, because
    :ref:`querysets-are-lazy`.

.. fieldlookup:: gt

gt
~~

Greater than.

Example::

    Entry.objects.filter(id__gt=4)

SQL equivalent::

    SELECT ... WHERE id > 4;

.. fieldlookup:: gte

gte
~~~

Greater than or equal to.

.. fieldlookup:: lt

lt
~~

Less than.

.. fieldlookup:: lte

lte
~~~

Less than or equal to.

.. fieldlookup:: startswith

startswith
~~~~~~~~~~

Case-sensitive starts-with.

Example::

    Entry.objects.filter(headline__startswith='Will')

SQL equivalent::

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

SQLite doesn't support case-sensitive ``LIKE`` statements; ``startswith`` acts
like ``istartswith`` for SQLite.

.. fieldlookup:: istartswith

istartswith
~~~~~~~~~~~

Case-insensitive starts-with.

Example::

    Entry.objects.filter(headline__istartswith='will')

SQL equivalent::

    SELECT ... WHERE headline ILIKE 'Will%';

.. admonition:: SQLite users

    When using the SQLite backend and Unicode (non-ASCII) strings, bear in
    mind the :ref:`database note <sqlite-string-matching>` about string
    comparisons.

.. fieldlookup:: endswith

endswith
~~~~~~~~

Case-sensitive ends-with.

Example::

    Entry.objects.filter(headline__endswith='cats')

SQL equivalent::

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

SQLite doesn't support case-sensitive ``LIKE`` statements; ``endswith`` acts
like ``iendswith`` for SQLite.

.. fieldlookup:: iendswith

iendswith
~~~~~~~~~

Case-insensitive ends-with.

Example::

    Entry.objects.filter(headline__iendswith='will')

SQL equivalent::

    SELECT ... WHERE headline ILIKE '%will'

.. admonition:: SQLite users

    When using the SQLite backend and Unicode (non-ASCII) strings, bear in
    mind the :ref:`database note <sqlite-string-matching>` about string
    comparisons.

.. fieldlookup:: range

range
~~~~~

Range test (inclusive).

Example::

    start_date = datetime.date(2005, 1, 1)
    end_date = datetime.date(2005, 3, 31)
    Entry.objects.filter(pub_date__range=(start_date, end_date))

SQL equivalent::

    SELECT ... WHERE pub_date BETWEEN '2005-01-01' and '2005-03-31';

You can use ``range`` anywhere you can use ``BETWEEN`` in SQL -- for dates,
numbers and even characters.

.. fieldlookup:: year

year
~~~~

For date/datetime fields, exact year match. Takes a four-digit year.

Example::

    Entry.objects.filter(pub_date__year=2005)

SQL equivalent::

    SELECT ... WHERE EXTRACT('year' FROM pub_date) = '2005';

(The exact SQL syntax varies for each database engine.)

.. fieldlookup:: month

month
~~~~~

For date/datetime fields, exact month match. Takes an integer 1 (January)
through 12 (December).

Example::

    Entry.objects.filter(pub_date__month=12)

SQL equivalent::

    SELECT ... WHERE EXTRACT('month' FROM pub_date) = '12';

(The exact SQL syntax varies for each database engine.)

.. fieldlookup:: day

day
~~~

For date/datetime fields, exact day match.

Example::

    Entry.objects.filter(pub_date__day=3)

SQL equivalent::

    SELECT ... WHERE EXTRACT('day' FROM pub_date) = '3';

(The exact SQL syntax varies for each database engine.)

Note this will match any record with a pub_date on the third day of the month,
such as January 3, July 3, etc.

.. fieldlookup:: week_day

week_day
~~~~~~~~

.. versionadded:: 1.1

For date/datetime fields, a 'day of the week' match.

Takes an integer value representing the day of week from 1 (Sunday) to 7
(Saturday).

Example::

    Entry.objects.filter(pub_date__week_day=2)

(No equivalent SQL code fragment is included for this lookup because
implementation of the relevant query varies among different database engines.)

Note this will match any record with a pub_date that falls on a Monday (day 2
of the week), regardless of the month or year in which it occurs. Week days
are indexed with day 1 being Sunday and day 7 being Saturday.

.. fieldlookup:: isnull

isnull
~~~~~~

Takes either ``True`` or ``False``, which correspond to SQL queries of
``IS NULL`` and ``IS NOT NULL``, respectively.

Example::

    Entry.objects.filter(pub_date__isnull=True)

SQL equivalent::

    SELECT ... WHERE pub_date IS NULL;

.. fieldlookup:: search

search
~~~~~~

A boolean full-text search, taking advantage of full-text indexing. This is
like ``contains`` but is significantly faster due to full-text indexing.

Example::

    Entry.objects.filter(headline__search="+Django -jazz Python")

SQL equivalent::

    SELECT ... WHERE MATCH(tablename, headline) AGAINST (+Django -jazz Python IN BOOLEAN MODE);

Note this is only available in MySQL and requires direct manipulation of the
database to add the full-text index. By default Django uses BOOLEAN MODE for
full text searches. `See the MySQL documentation for additional details.
<http://dev.mysql.com/doc/refman/5.1/en/fulltext-boolean.html>`_


.. fieldlookup:: regex

regex
~~~~~

.. versionadded:: 1.0

Case-sensitive regular expression match.

The regular expression syntax is that of the database backend in use.
In the case of SQLite, which has no built in regular expression support,
this feature is provided by a (Python) user-defined REGEXP function, and
the regular expression syntax is therefore that of Python's ``re`` module.

Example::

    Entry.objects.get(title__regex=r'^(An?|The) +')

SQL equivalents::

    SELECT ... WHERE title REGEXP BINARY '^(An?|The) +'; -- MySQL

    SELECT ... WHERE REGEXP_LIKE(title, '^(an?|the) +', 'c'); -- Oracle

    SELECT ... WHERE title ~ '^(An?|The) +'; -- PostgreSQL

    SELECT ... WHERE title REGEXP '^(An?|The) +'; -- SQLite

Using raw strings (e.g., ``r'foo'`` instead of ``'foo'``) for passing in the
regular expression syntax is recommended.

.. fieldlookup:: iregex

iregex
~~~~~~

.. versionadded:: 1.0

Case-insensitive regular expression match.

Example::

    Entry.objects.get(title__iregex=r'^(an?|the) +')

SQL equivalents::

    SELECT ... WHERE title REGEXP '^(an?|the) +'; -- MySQL

    SELECT ... WHERE REGEXP_LIKE(title, '^(an?|the) +', 'i'); -- Oracle

    SELECT ... WHERE title ~* '^(an?|the) +'; -- PostgreSQL

    SELECT ... WHERE title REGEXP '(?i)^(an?|the) +'; -- SQLite

.. _aggregation-functions:

Aggregation Functions
---------------------

.. versionadded:: 1.1

Django provides the following aggregation functions in the
``django.db.models`` module. For details on how to use these
aggregate functions, see
:doc:`the topic guide on aggregation </topics/db/aggregation>`.

Avg
~~~

.. class:: Avg(field)

Returns the mean value of the given field.

    * Default alias: ``<field>__avg``
    * Return type: float

Count
~~~~~

.. class:: Count(field, distinct=False)

Returns the number of objects that are related through the provided field.

    * Default alias: ``<field>__count``
    * Return type: integer

Has one optional argument:

.. attribute:: distinct

    If distinct=True, the count will only include unique instances. This has
    the SQL equivalent of ``COUNT(DISTINCT field)``. Default value is ``False``.

Max
~~~

.. class:: Max(field)

Returns the maximum value of the given field.

    * Default alias: ``<field>__max``
    * Return type: same as input field

Min
~~~

.. class:: Min(field)

Returns the minimum value of the given field.

    * Default alias: ``<field>__min``
    * Return type: same as input field

StdDev
~~~~~~

.. class:: StdDev(field, sample=False)

Returns the standard deviation of the data in the provided field.

    * Default alias: ``<field>__stddev``
    * Return type: float

Has one optional argument:

.. attribute:: sample

    By default, ``StdDev`` returns the population standard deviation. However,
    if ``sample=True``, the return value will be the sample standard deviation.

.. admonition:: SQLite

    SQLite doesn't provide ``StdDev`` out of the box. An implementation is
    available as an extension module for SQLite. Consult the SQlite
    documentation for instructions on obtaining and installing this extension.

Sum
~~~

.. class:: Sum(field)

Computes the sum of all values of the given field.

    * Default alias: ``<field>__sum``
    * Return type: same as input field

Variance
~~~~~~~~

.. class:: Variance(field, sample=False)

Returns the variance of the data in the provided field.

    * Default alias: ``<field>__variance``
    * Return type: float

Has one optional argument:

.. attribute:: sample

    By default, ``Variance`` returns the population variance. However,
    if ``sample=True``, the return value will be the sample variance.

.. admonition:: SQLite

    SQLite doesn't provide ``Variance`` out of the box. An implementation is
    available as an extension module for SQLite. Consult the SQlite
    documentation for instructions on obtaining and installing this extension.
