====================== 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 ` and :doc:`database query ` 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 ` presented in the :doc:`database query guide `. .. _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. Slicing an unevaluated ``QuerySet`` usually returns another unevaluated ``QuerySet``, but Django will execute the database query if you use the "step" parameter of slice syntax, and will return a list. Slicing a ``QuerySet`` that has been evaluated (partially or fully) also returns a list. * **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() ` (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 `. 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 `. 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 ` 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([*fields]) 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. .. versionadded:: 1.4 As of Django 1.4, you can pass positional arguments (``*fields``) in order to specify the names of fields to which the ``DISTINCT`` should apply. This translates to a ``SELECT DISTINCT ON`` SQL query. Here's the difference. For a normal ``distinct()`` call, the database compares *each* field in each row when determining which rows are distinct. For a ``distinct()`` call with specified field names, the database will only compare the specified field names. .. note:: This ability to specify field names is only available in PostgreSQL. .. note:: When you specify field names, you *must* provide an ``order_by()`` in the QuerySet, and the fields in ``order_by()`` must start with the fields in ``distinct()``, in the same order. For example, ``SELECT DISTINCT ON (a)`` gives you the first row for each value in column ``a``. If you don't specify an order, you'll get some arbitrary row. Examples:: >>> Author.objects.distinct() [...] >>> Entry.objects.order_by('pub_date').distinct('pub_date') [...] >>> Entry.objects.order_by('blog').distinct('blog') [...] >>> Entry.objects.order_by('author', 'pub_date').distinct('author', 'pub_date') [...] >>> Entry.objects.order_by('blog__name', 'mod_date').distinct('blog__name', 'mod_date') [...] >>> Entry.objects.order_by('author', 'pub_date').distinct('author') [...] 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') [] # 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)] .. warning:: When :doc:`time zone support ` is enabled, Django uses UTC in the database connection, which means the aggregation is performed in UTC. This is a known limitation of the current implementation. 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 ` 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. ``prefetch_related`` in most cases will be implemented using a SQL query that uses the 'IN' operator. This means that for a large QuerySet a large 'IN' clause could be generated, which, depending on the database, might have performance problems of its own when it comes to parsing or executing the SQL query. Always profile for your use case! Note that if you use ``iterator()`` to run the query, ``prefetch_related()`` calls will be ignored since these two optimizations do not make sense together. 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") 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 :meth:`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 :meth:`select_related()` to retrieve related models, 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, :meth:`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. Even if you think you are in the advanced use-case situation, **only use defer() when you cannot, at queryset load time, determine if you will need the extra fields or not**. If you are frequently loading and using a particular subset of your data, the best choice you can make is to normalize your models and put the non-loaded data into a separate model (and database table). If the columns *must* stay in the one table for some reason, create a model with ``Meta.managed = False`` (see the :attr:`managed attribute ` documentation) containing just the fields you normally need to load and use that where you might otherwise call ``defer()``. This makes your code more explicit to the reader, is slightly faster and consumes a little less memory in the Python process. only ~~~~ .. method:: only(*fields) The ``only()`` method is more or less the opposite of :meth:`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 can result in simpler code. Suppose 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") All of the cautions in the note for the :meth:`defer` documentation apply to ``only()`` as well. Use it cautiously and only after exhausting your other options. 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') select_for_update ~~~~~~~~~~~~~~~~~ .. method:: select_for_update(nowait=False) .. versionadded:: 1.4 Returns a queryset that will lock rows until the end of the transaction, generating a ``SELECT ... FOR UPDATE`` SQL statement on supported databases. For example:: entries = Entry.objects.select_for_update().filter(author=request.user) All matched entries will be locked until the end of the transaction block, meaning that other transactions will be prevented from changing or acquiring locks on them. Usually, if another transaction has already acquired a lock on one of the selected rows, the query will block until the lock is released. If this is not the behavior you want, call ``select_for_update(nowait=True)``. This will make the call non-blocking. If a conflicting lock is already acquired by another transaction, :exc:`~django.db.DatabaseError` will be raised when the queryset is evaluated. Note that using ``select_for_update()`` will cause the current transaction to be considered dirty, if under transaction management. This is to ensure that Django issues a ``COMMIT`` or ``ROLLBACK``, releasing any locks held by the ``SELECT FOR UPDATE``. Currently, the ``postgresql_psycopg2``, ``oracle``, and ``mysql`` database backends support ``select_for_update()``. However, MySQL has no support for the ``nowait`` argument. Obviously, users of external third-party backends should check with their backend's documentation for specifics in those cases. Passing ``nowait=True`` to ``select_for_update`` using database backends that do not support ``nowait``, such as MySQL, will cause a :exc:`~django.db.DatabaseError` to be raised. This is in order to prevent code unexpectedly blocking. Using ``select_for_update`` on backends which do not support ``SELECT ... FOR UPDATE`` (such as SQLite) will have no effect. 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 :exc:`~django.core.exceptions.MultipleObjectsReturned` if more than one object was found. The :exc:`~django.core.excpetions.MultipleObjectsReturned` exception is an attribute of the model class. ``get()`` raises a :exc:`~django.core.exceptions.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 :exc:`~django.core.exceptions.DoesNotExist` exception inherits from :exc:`django.core.exceptions.ObjectDoesNotExist`, so you can target multiple :exc:`~django.core.exceptions.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 ` 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:`~django.db.IntegrityError` since primary keys must be unique. 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 :meth:`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 behavior to :meth:`create()` when you're using manually specified primary keys. If an object needs to be created and the key already exists in the database, an :exc:`~django.db.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 bulk_create ~~~~~~~~~~~ .. method:: bulk_create(objs) .. versionadded:: 1.4 This method inserts the provided list of objects into the database in an efficient manner (generally only 1 query, no matter how many objects there are):: >>> Entry.objects.bulk_create([ ... Entry(headline="Django 1.0 Released"), ... Entry(headline="Django 1.1 Announced"), ... Entry(headline="Breaking: Django is awesome") ... ]) This has a number of caveats though: * The model's ``save()`` method will not be called, and the ``pre_save`` and ``post_save`` signals will not be sent. * It does not work with child models in a multi-table inheritance scenario. * If the model's primary key is an :class:`~django.db.models.AutoField` it does not retrieve and set the primary key attribute, as ``save()`` does. .. admonition:: Limits of SQLite SQLite sets a limit on the number of parameters per SQL statement. The maximum is defined by the SQLITE_MAX_VARIABLE_NUMBER_ compilation option, which defaults to 999. For instance, if your model has 8 fields (including the primary key), you cannot create more than 999 // 8 = 124 instances at a time. If you exceed this limit, you'll get an exception:: django.db.utils.DatabaseError: too many SQL variables If your application's performance requirements exceed SQLite's limits, you should switch to another database engine, such as PostgreSQL. .. _SQLITE_MAX_VARIABLE_NUMBER: http://sqlite.org/limits.html#max_variable_number count ~~~~~ .. method:: count() Returns an integer representing the number of objects in the database matching the ``QuerySet``. The ``count()`` method 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() A ``count()`` call 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.objects.in_bulk([1, 2]) {1: , 2: } >>> 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 (see :pep:`234`) over the results. A ``QuerySet`` typically caches its results internally so that repeated evaluations do not result in additional queries. In contrast, ``iterator()`` will read results directly, without doing any caching at the ``QuerySet`` level (internally, the default iterator calls ``iterator()`` and caches the return value). For a ``QuerySet`` which returns a large number of objects that you only need to access once, this can 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. Also, use of ``iterator()`` causes previous ``prefetch_related()`` calls to be ignored since these two optimizations do not make sense together. 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 :ref:`Meta ` specifies :attr:`~django.db.models.Options.get_latest_by`, you can leave off the ``field_name`` argument to ``latest()``. Django will use the field specified in :attr:`~django.db.models.Options.get_latest_by` by default. Like :meth:`get()`, ``latest()`` raises :exc:`~django.core.exceptions.DoesNotExist` if there is no object with the given parameters. Note ``latest()`` exists purely for convenience and readability. aggregate ~~~~~~~~~ .. method:: aggregate(*args, **kwargs) 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 a name generated for them based upon the name of the aggregate function and the model field that is being aggregated. For example, when you are working with 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 `. 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 (one query for the existence check plus an extra one to later retrieve the results) than simply using ``bool(some_query_set)``, which retrieves the results and then checks if any were returned. update ~~~~~~ .. method:: update(**kwargs) Performs an SQL update query for the specified fields, and returns the number of rows affected. For example, to turn comments off for all blog entries published in 2010, you could do this:: >>> Entry.objects.filter(pub_date__year=2010).update(comments_on=False) (This assumes your ``Entry`` model has fields ``pub_date`` and ``comments_on``.) You can update multiple fields — there's no limit on how many. For example, here we update the ``comments_on`` and ``headline`` fields:: >>> Entry.objects.filter(pub_date__year=2010).update(comments_on=False, headline='This is old') 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, not on related models. You can't do this, for example:: >>> Entry.objects.update(blog__name='foo') # Won't work! Filtering based on related fields is still possible, though:: >>> Entry.objects.filter(blog__id=1).update(comments_on=True) You cannot call ``update()`` on a :class:`.QuerySet` that has had a slice taken or can otherwise no longer be filtered. The ``update()`` method returns the number of affected rows:: >>> Entry.objects.filter(id=64).update(comments_on=True) 1 >>> Entry.objects.filter(slug='nonexistent-slug').update(comments_on=True) 0 >>> Entry.objects.filter(pub_date__year=2010).update(comments_on=False) 132 If you're just updating a record and don't need to do anything with the model object, the most efficient approach is to call ``update()``, rather than loading the model object into memory. For example, instead of doing this:: e = Entry.objects.get(id=10) e.comments_on = False e.save() ...do this:: Entry.objects.filter(id=10).update(comments_on=False) Using ``update()`` also prevents a race condition wherein something might change in your database in the short period of time between loading the object and calling ``save()``. Finally, realize that ``update()`` does an update at the SQL level and, thus, does not call any ``save()`` methods on your models, nor does it emit the :attr:`~django.db.models.signals.pre_save` or :attr:`~django.db.models.signals.post_save` signals (which are a consequence of calling :meth:`Model.save() <~django.db.models.Model.save()>`). If you want to update a bunch of records for a model that has a custom :meth:`~django.db.models.Model.save()`` method, loop over them and call :meth:`~django.db.models.Model.save()`, like this:: for e in Entry.objects.filter(pub_date__year=2010): e.comments_on = False e.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() By default, Django's :class:`~django.db.models.ForeignKey` 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() .. versionadded:: 1.3 This cascade behavior is customizable via the :attr:`~django.db.models.ForeignKey.on_delete` argument to the :class:`~django.db.models.ForeignKey`. 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 :meth:`filter()`, :meth:`exclude()` and :meth:`get()`. For an introduction, see :ref:`models and database queries documentation `. .. fieldlookup:: exact exact ~~~~~ Exact match. If the value provided for comparison is ``None``, it will be interpreted as an SQL ``NULL`` (see :lookup:`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; .. 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 ` in the :doc:`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 ` 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 ``'Lennon honored today'`` but not ``'lennon honored today'``. .. admonition:: SQLite users SQLite doesn't support case-sensitive ``LIKE`` statements; ``contains`` acts like ``icontains`` for SQLite. See the :ref:`database note ` for more information. .. 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 ` 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) .. 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. 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) .. 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 ` about string comparisons. .. fieldlookup:: endswith endswith ~~~~~~~~ Case-sensitive ends-with. Example:: Entry.objects.filter(headline__endswith='cats') SQL equivalent:: SELECT ... WHERE headline LIKE '%cats'; .. admonition:: SQLite users SQLite doesn't support case-sensitive ``LIKE`` statements; ``endswith`` acts like ``iendswith`` for SQLite. Refer to the :ref:`database note ` documentation for more. .. 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 ` 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 pub_date BETWEEN '2005-01-01' AND '2005-12-31 23:59:59.999999'; (The exact SQL syntax varies for each database engine.) .. fieldlookup:: month month ~~~~~ For date and datetime fields, an 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 and datetime fields, an 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 ~~~~~~~~ For date and 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. .. warning:: When :doc:`time zone support ` is enabled, Django uses UTC in the database connection, which means the ``year``, ``month``, ``day`` and ``week_day`` lookups are performed in UTC. This is a known limitation of the current implementation. .. 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 :lookup:`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. .. _MySQL documentation: http://dev.mysql.com/doc/refman/5.1/en/fulltext-boolean.html> .. fieldlookup:: regex regex ~~~~~ 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 ~~~~~~ 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 --------------------- .. currentmodule:: django.db.models 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 `. Avg ~~~ .. class:: Avg(field) Returns the mean value of the given field, which must be numeric. * Default alias: ``__avg`` * Return type: ``float`` Count ~~~~~ .. class:: Count(field, distinct=False) Returns the number of objects that are related through the provided field. * Default alias: ``__count`` * Return type: ``int`` Has one optional argument: .. attribute:: distinct If ``distinct=True``, the count will only include unique instances. This is the SQL equivalent of ``COUNT(DISTINCT )``. The default value is ``False``. Max ~~~ .. class:: Max(field) Returns the maximum value of the given field. * Default alias: ``__max`` * Return type: same as input field Min ~~~ .. class:: Min(field) Returns the minimum value of the given field. * Default alias: ``__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: ``__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: ``__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: ``__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. .. _SQLite documentation: http://www.sqlite.org/contrib