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querysets.txt
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======================
QuerySet API reference
======================
.. currentmodule:: django.db.models.query
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 :meth:`exists() <QuerySet.exists>` (see below).
.. _pickling QuerySets:
Pickling QuerySets
------------------
If you :mod:`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.
.. _queryset-api:
QuerySet API
============
Though you usually won't create one manually — you'll go through a
:class:`~django.db.models.Manager` — here's the formal declaration of a
``QuerySet``:
.. class:: QuerySet([model=None, query=None, using=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. These methods are covered in
detail later in this section.
The ``QuerySet`` class has two public attributes you can use for
introspection:
.. attribute:: ordered
``True`` if the ``QuerySet`` is ordered — i.e. has an
:meth:`order_by()` clause or a default ordering on the model.
``False`` otherwise.
.. attribute:: db
The database that will be used if this query is executed now.
.. note::
The ``query`` parameter to :class:`QuerySet` exists so that specialized
query subclasses such as
:class:`~django.contrib.gis.db.models.GeoQuerySet` can reconstruct
internal query state. The value of the parameter is an opaque
representation of that query state and is not part of a public API.
To put it simply: if you need to ask, you don't need to use it.
.. currentmodule:: django.db.models.query.QuerySet
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)
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 :attr:`Meta.ordering
<django.db.models.Options.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
:meth:`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 :class:`~django.db.models.ManyToManyField` 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.
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 :meth:`order_by()` with no parameters.
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()
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 :meth:`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 :meth:`values()` query to restrict the columns
selected, the columns used in any :meth:`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
:meth:`values()` together, be careful when ordering by fields not in the
:meth:`values()` call.
values
~~~~~~
.. method:: values(*fields)
Returns a ``ValuesQuerySet`` — a ``QuerySet`` subclass 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.'}]
The ``values()`` method 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 few subtleties that are worth mentioning:
* 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 :meth:`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 :meth:`extra()` call,
any fields defined by a ``select`` argument in the :meth:`extra()` must
be explicitly included in the ``values()`` call. Any :meth:`extra()` call
made after a ``values()`` call will have its extra selected fields
ignored.
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.
.. versionchanged:: 1.3
The ``values()`` method previously did not return anything for
:class:`~django.db.models.ManyToManyField` attributes and would raise an error
if you tried to pass this type of field to it.
This restriction has been lifted, and you can now also refer to fields on
related models with reverse relations through ``OneToOneField``, ``ForeignKey``
and ``ManyToManyField`` attributes::
Blog.objects.values('name', 'entry__headline')
[{'name': 'My blog', 'entry__headline': 'An entry'},
{'name': 'My blog', 'entry__headline': 'Another entry'}, ...]
.. warning::
Because :class:`~django.db.models.ManyToManyField` attributes and reverse
relations can have multiple related rows, including these can have a
multiplier effect on the size of your result set. This will be especially
pronounced if you include multiple such fields in your ``values()`` query,
in which case all possible combinations will be returned.
values_list
~~~~~~~~~~~
.. method:: values_list(*fields)
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()
Returns an ``EmptyQuerySet`` — a ``QuerySet`` subclass 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()
Returns a *copy* of the current ``QuerySet`` (or ``QuerySet`` subclass). This
can be useful in situations where you might want to pass in either a model
manager or a ``QuerySet`` and do further filtering on the result. After calling
``all()`` on either object, you'll definitely have a ``QuerySet`` to work with.
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):
# ...
pass
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 :class:`~django.db.models.ForeignKey` or
:class:`~django.db.models.OneToOneField` relation in the list of fields
passed to ``select_related()``. This includes foreign keys that have
``null=True`` (which are omitted in a no-parameter ``select_related()`` call).
It's an error to use both a list of fields and the ``depth`` parameter in the
same ``select_related()`` call; they are conflicting options.
.. versionchanged:: 1.2
You can also refer to the reverse direction of a
:class:`~django.db.models.OneToOneField` in the list of fields passed to
``select_related`` — that is, you can traverse a
:class:`~django.db.models.OneToOneField` back to the object on which the field
is defined. Instead of specifying the field name, use the :attr:`related_name
<django.db.models.ForeignKey.related_name>` for the field on the related object.
A :class:`~django.db.models.OneToOneField` is not traversed in the reverse
direction if you are performing a depth-based ``select_related()`` call.
prefetch_related
~~~~~~~~~~~~~~~~
.. method:: prefetch_related(*lookups)
.. versionadded:: 1.4
Returns a ``QuerySet`` that will automatically retrieve, in a single batch,
related objects for each of the specified lookups.
This has a similar purpose to ``select_related``, in that both are designed to
stop the deluge of database queries that is caused by accessing related objects,
but the strategy is quite different.
``select_related`` works by creating a SQL join and including the fields of the
related object in the SELECT statement. For this reason, ``select_related`` gets
the related objects in the same database query. However, to avoid the much
larger result set that would result from joining across a 'many' relationship,
``select_related`` is limited to single-valued relationships - foreign key and
one-to-one.
``prefetch_related``, on the other hand, does a separate lookup for each
relationship, and does the 'joining' in Python. This allows it to prefetch
many-to-many and many-to-one objects, which cannot be done using
``select_related``, in addition to the foreign key and one-to-one relationships
that are supported by ``select_related``. It also supports prefetching of
:class:`~django.contrib.contenttypes.generic.GenericRelation` and
:class:`~django.contrib.contenttypes.generic.GenericForeignKey`.
For example, suppose you have these models::
class Topping(models.Model):
name = models.CharField(max_length=30)
class Pizza(models.Model):
name = models.CharField(max_length=50)
toppings = models.ManyToManyField(Topping)
def __unicode__(self):
return u"%s (%s)" % (self.name, u", ".join([topping.name
for topping in self.toppings.all()]))
and run this code::
>>> Pizza.objects.all()
[u"Hawaiian (ham, pineapple)", u"Seafood (prawns, smoked salmon)"...
The problem with this code is that it will run a query on the Toppings table for
**every** item in the Pizza ``QuerySet``. Using ``prefetch_related``, this can
be reduced to two:
>>> Pizza.objects.all().prefetch_related('toppings')
All the relevant toppings will be fetched in a single query, and used to make
``QuerySets`` that have a pre-filled cache of the relevant results. These
``QuerySets`` are then used in the ``self.toppings.all()`` calls.
The additional queries are executed after the QuerySet has begun to be evaluated
and the primary query has been executed. Note that the result cache of the
primary QuerySet and all specified related objects will then be fully loaded
into memory, which is often avoided in other cases - even after a query has been
executed in the database, QuerySet normally tries to make uses of chunking
between the database to avoid loading all objects into memory before you need
them.
Also remember that, as always with QuerySets, any subsequent chained methods
which imply a different database query will ignore previously cached results,
and retrieve data using a fresh database query. So, if you write the following:
>>> pizzas = Pizza.objects.prefetch_related('toppings')
>>> [list(pizza.toppings.filter(spicy=True)) for pizza in pizzas]
...then the fact that ``pizza.toppings.all()`` has been prefetched will not help
you - in fact it hurts performance, since you have done a database query that
you haven't used. So use this feature with caution!
You can also use the normal join syntax to do related fields of related
fields. Suppose we have an additional model to the example above::
class Restaurant(models.Model):
pizzas = models.ManyToMany(Pizza, related_name='restaurants')
best_pizza = models.ForeignKey(Pizza, related_name='championed_by')
The following are all legal:
>>> Restaurant.objects.prefetch_related('pizzas__toppings')
This will prefetch all pizzas belonging to restaurants, and all toppings
belonging to those pizzas. This will result in a total of 3 database queries -
one for the restaurants, one for the pizzas, and one for the toppings.
>>> Restaurant.objects.prefetch_related('best_pizza__toppings')
This will fetch the best pizza and all the toppings for the best pizza for each
restaurant. This will be done in 3 database queries - one for the restaurants,
one for the 'best pizzas', and one for one for the toppings.
Of course, the ``best_pizza`` relationship could also be fetched using
``select_related`` to reduce the query count to 2:
>>> Restaurant.objects.select_related('best_pizza').prefetch_related('best_pizza__toppings')
Since the prefetch is executed after the main query (which includes the joins
needed by ``select_related``), it is able to detect that the ``best_pizza``
objects have already been fetched, and it will skip fetching them again.
Chaining ``prefetch_related`` calls will accumulate the lookups that are
prefetched. To clear any ``prefetch_related`` behavior, pass `None` as a
parameter::
>>> non_prefetched = qs.prefetch_related(None)
One difference to note when using ``prefetch_related`` is that objects created
by a query can be shared between the different objects that they are related to
i.e. a single Python model instance can appear at more than one point in the
tree of objects that are returned. This will normally happen with foreign key
relationships. Typically this behavior will not be a problem, and will in fact
save both memory and CPU time.
While ``prefetch_related`` supports prefetching ``GenericForeignKey``
relationships, the number of queries will depend on the data. Since a
``GenericForeignKey`` can reference data in multiple tables, one query per table
referenced is needed, rather than one query for all the items. There could be
additional queries on the ``ContentType`` table if the relevant rows have not
already been fetched.
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 parentheses 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.
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
:class:`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
:meth:`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)
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 know
if you need those particular fields when you initially fetch the data, 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")