QuerySet API reference | Django documentation | Django

These methods do not run database queries, therefore they are safe to run in asynchronous code, and do not have separate asynchronous versions.

filter()¶

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.

If you need to execute more complex queries (for example, queries with OR statements), you can use Q objects (*args).

exclude()¶

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.

If you need to execute more complex queries (for example, queries with OR statements), you can use Q objects (*args).

annotate()¶

Annotates each object in the QuerySet with the provided list of query expressions. An expression may be a simple value, a reference to a field on the model (or any related models), or an aggregate expression (averages, sums, etc.) that has 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. Only aggregate expressions that reference a single field can be anonymous arguments. Everything else must be a keyword argument.

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

>>> from django.db.models import Count
>>> 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 the topic guide on Aggregation.

alias()¶

Same as annotate(), but instead of annotating objects in the QuerySet, saves the expression for later reuse with other QuerySet methods. This is useful when the result of the expression itself is not needed but it is used for filtering, ordering, or as a part of a complex expression. Not selecting the unused value removes redundant work from the database which should result in better performance.

For example, if you want to find blogs with more than 5 entries, but are not interested in the exact number of entries, you could do this:

>>> from django.db.models import Count
>>> blogs = Blog.objects.alias(entries=Count('entry')).filter(entries__gt=5)

alias() can be used in conjunction with annotate(), exclude(), filter(), order_by(), and update(). To use aliased expression with other methods (e.g. aggregate()), you must promote it to an annotation:

Blog.objects.alias(entries=Count('entry')).annotate(
    entries=F('entries'),
).aggregate(Sum('entries'))

filter() and order_by() can take expressions directly, but expression construction and usage often does not happen in the same place (for example, QuerySet method creates expressions, for later use in views). alias() allows building complex expressions incrementally, possibly spanning multiple methods and modules, refer to the expression parts by their aliases and only use annotate() for the final result.

order_by()¶

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

Example:

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

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

Entry.objects.order_by('?')

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

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

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

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

Entry.objects.order_by('blog')

…is identical to:

Entry.objects.order_by('blog__id')

If Blog had ordering = ['name'], then the first queryset would be identical to:

Entry.objects.order_by('blog__name')

You can also order by query expressions by calling asc() or desc() on the expression:

Entry.objects.order_by(Coalesce('summary', 'headline').desc())

asc() and desc() have arguments (nulls_first and nulls_last) that control how null values are sorted.

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

class Event(Model):
   parent = models.ForeignKey(
       'self',
       on_delete=models.CASCADE,
       related_name='children',
   )
   date = models.DateField()

Event.objects.order_by('children__date')

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.

You can order by a field converted to lowercase with Lower which will achieve case-consistent ordering:

Entry.objects.order_by(Lower('headline').desc())

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

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

Each order_by() call will clear any previous ordering. For example, this query will be ordered by pub_date and not headline:

Entry.objects.order_by('headline').order_by('pub_date')

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 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()¶

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().

On PostgreSQL only, 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.

Examples (those after the first will only work on PostgreSQL):

>>> 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')
[...]

Entry.objects.order_by('blog').distinct('blog')

values()¶

Returns a QuerySet that returns dictionaries, rather than model instances, when used as an iterable.

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')
<QuerySet [<Blog: Beatles Blog>]>

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

The values() method also takes optional keyword arguments, **expressions, which are passed through to annotate():

>>> from django.db.models.functions import Lower
>>> Blog.objects.values(lower_name=Lower('name'))
<QuerySet [{'lower_name': 'beatles blog'}]>

You can use built-in and custom lookups in ordering. For example:

>>> from django.db.models import CharField
>>> from django.db.models.functions import Lower
>>> CharField.register_lookup(Lower)
>>> Blog.objects.values('name__lower')
<QuerySet [{'name__lower': 'beatles blog'}]>

An aggregate within a values() clause is applied before other arguments within the same values() clause. If you need to group by another value, add it to an earlier values() clause instead. For example:

>>> from django.db.models import Count
>>> Blog.objects.values('entry__authors', entries=Count('entry'))
<QuerySet [{'entry__authors': 1, 'entries': 20}, {'entry__authors': 1, 'entries': 13}]>
>>> Blog.objects.values('entry__authors').annotate(entries=Count('entry'))
<QuerySet [{'entry__authors': 1, 'entries': 33}]>

A few subtleties that are worth mentioning:

• If you have a field called foo that is a 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()
  <QuerySet [{'blog_id': 1, 'headline': 'First Entry', ...}, ...]>
  
  >>> Entry.objects.values('blog')
  <QuerySet [{'blog': 1}, ...]>
  
  >>> Entry.objects.values('blog_id')
  <QuerySet [{'blog_id': 1}, ...]>
  

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

• If you use a values() clause after an extra() call, any fields defined by a select argument in the extra() must be explicitly included in the values() call. Any extra() call made after a values() call will have its extra selected fields ignored.

• Calling only() and defer() after values() doesn’t make sense, so doing so will raise a TypeError.

• Combining transforms and aggregates requires the use of two annotate() calls, either explicitly or as keyword arguments to values(). As above, if the transform has been registered on the relevant field type the first annotate() can be omitted, thus the following examples are equivalent:

  >>> from django.db.models import CharField, Count
  >>> from django.db.models.functions import Lower
  >>> CharField.register_lookup(Lower)
  >>> Blog.objects.values('entry__authors__name__lower').annotate(entries=Count('entry'))
  <QuerySet [{'entry__authors__name__lower': 'test author', 'entries': 33}]>
  >>> Blog.objects.values(
  ...     entry__authors__name__lower=Lower('entry__authors__name')
  ... ).annotate(entries=Count('entry'))
  <QuerySet [{'entry__authors__name__lower': 'test author', 'entries': 33}]>
  >>> Blog.objects.annotate(
  ...     entry__authors__name__lower=Lower('entry__authors__name')
  ... ).values('entry__authors__name__lower').annotate(entries=Count('entry'))
  <QuerySet [{'entry__authors__name__lower': 'test author', 'entries': 33}]>
  

It 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 that you can call filter(), order_by(), etc. after the values() call, that means that 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.

You can also refer to fields on related models with reverse relations through OneToOneField, ForeignKey and ManyToManyField attributes:

>>> Blog.objects.values('name', 'entry__headline')
<QuerySet [{'name': 'My blog', 'entry__headline': 'An entry'},
     {'name': 'My blog', 'entry__headline': 'Another entry'}, ...]>

values_list()¶

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 or expression passed into the values_list() call — so the first item is the first field, etc. For example:

>>> Entry.objects.values_list('id', 'headline')
<QuerySet [(1, 'First entry'), ...]>
>>> from django.db.models.functions import Lower
>>> Entry.objects.values_list('id', Lower('headline'))
<QuerySet [(1, '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')
<QuerySet[(1,), (2,), (3,), ...]>

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

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

You can pass named=True to get results as a namedtuple():

>>> Entry.objects.values_list('id', 'headline', named=True)
<QuerySet [Row(id=1, headline='First entry'), ...]>

Using a named tuple may make use of the results more readable, at the expense of a small performance penalty for transforming the results into a named tuple.

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.

A common need is to get a specific field value of a certain model instance. To achieve that, use values_list() followed by a get() call:

>>> Entry.objects.values_list('headline', flat=True).get(pk=1)
'First entry'

values() and values_list() are both intended as optimizations for a specific use case: retrieving a subset of data without the overhead of creating a model instance. This metaphor falls apart when dealing with many-to-many and other multivalued relations (such as the one-to-many relation of a reverse foreign key) because the “one row, one object” assumption doesn’t hold.

For example, notice the behavior when querying across a ManyToManyField:

>>> Author.objects.values_list('name', 'entry__headline')
<QuerySet [('Noam Chomsky', 'Impressions of Gaza'),
 ('George Orwell', 'Why Socialists Do Not Believe in Fun'),
 ('George Orwell', 'In Defence of English Cooking'),
 ('Don Quixote', None)]>

Authors with multiple entries appear multiple times and authors without any entries have None for the entry headline.

Similarly, when querying a reverse foreign key, None appears for entries not having any author:

>>> Entry.objects.values_list('authors')
<QuerySet [('Noam Chomsky',), ('George Orwell',), (None,)]>

dates()¶

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

field should be the name of a DateField of your model. kind should be either "year", "month", "week", or "day". Each datetime.date 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.
• "week" returns a list of all distinct year/week values for the field. All dates will be a Monday.
• "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.date(2005, 1, 1)]
>>> Entry.objects.dates('pub_date', 'month')
[datetime.date(2005, 2, 1), datetime.date(2005, 3, 1)]
>>> Entry.objects.dates('pub_date', 'week')
[datetime.date(2005, 2, 14), datetime.date(2005, 3, 14)]
>>> Entry.objects.dates('pub_date', 'day')
[datetime.date(2005, 2, 20), datetime.date(2005, 3, 20)]
>>> Entry.objects.dates('pub_date', 'day', order='DESC')
[datetime.date(2005, 3, 20), datetime.date(2005, 2, 20)]
>>> Entry.objects.filter(headline__contains='Lennon').dates('pub_date', 'day')
[datetime.date(2005, 3, 20)]

datetimes()¶

Returns 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_name should be the name of a DateTimeField of your model.

kind should be either "year", "month", "week", "day", "hour", "minute", or "second". Each datetime.datetime object in the result list is “truncated” to the given type.

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

tzinfo defines the time zone to which datetimes are converted prior to truncation. Indeed, a given datetime has different representations depending on the time zone in use. This parameter must be a datetime.tzinfo object. If it’s None, Django uses the current time zone. It has no effect when USE_TZ is False.

is_dst indicates whether or not pytz should interpret nonexistent and ambiguous datetimes in daylight saving time. By default (when is_dst=None), pytz raises an exception for such datetimes.

none()¶

Calling none() will create a queryset that never returns any objects and no query will be executed when accessing the results. A qs.none() queryset is an instance of EmptyQuerySet.

Examples:

>>> Entry.objects.none()
<QuerySet []>
>>> from django.db.models.query import EmptyQuerySet
>>> isinstance(Entry.objects.none(), EmptyQuerySet)
True

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.

When a QuerySet is evaluated, it typically caches its results. If the data in the database might have changed since a QuerySet was evaluated, you can get updated results for the same query by calling all() on a previously evaluated QuerySet.

union()¶

Uses SQL’s UNION operator to combine the results of two or more QuerySets. For example:

>>> qs1.union(qs2, qs3)

The UNION operator selects only distinct values by default. To allow duplicate values, use the all=True argument.

union(), intersection(), and difference() return model instances of the type of the first QuerySet even if the arguments are QuerySets of other models. Passing different models works as long as the SELECT list is the same in all QuerySets (at least the types, the names don’t matter as long as the types are in the same order). In such cases, you must use the column names from the first QuerySet in QuerySet methods applied to the resulting QuerySet. For example:

>>> qs1 = Author.objects.values_list('name')
>>> qs2 = Entry.objects.values_list('headline')
>>> qs1.union(qs2).order_by('name')

In addition, only LIMIT, OFFSET, COUNT(*), ORDER BY, and specifying columns (i.e. slicing, count(), exists(), order_by(), and values()/values_list()) are allowed on the resulting QuerySet. Further, databases place restrictions on what operations are allowed in the combined queries. For example, most databases don’t allow LIMIT or OFFSET in the combined queries.

intersection()¶

Uses SQL’s INTERSECT operator to return the shared elements of two or more QuerySets. For example:

>>> qs1.intersection(qs2, qs3)

See union() for some restrictions.

difference()¶

Uses SQL’s EXCEPT operator to keep only elements present in the QuerySet but not in some other QuerySets. For example:

>>> qs1.difference(qs2, qs3)

See union() for some restrictions.

select_related()¶

Returns a QuerySet that will “follow” foreign-key relationships, selecting additional related-object data when it executes its query. This is a performance booster which results in a single more complex query 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('blog').get(id=5)

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

You can use select_related() with any queryset of objects:

from django.utils import timezone

# Find all the blogs with entries scheduled to be published in the future.
blogs = set()

for e in Entry.objects.filter(pub_date__gt=timezone.now()).select_related('blog'):
    # Without select_related(), this would make a database query for each
    # loop iteration in order to fetch the related blog for each entry.
    blogs.add(e.blog)

The order of filter() and select_related() chaining isn’t important. These querysets are equivalent:

Entry.objects.filter(pub_date__gt=timezone.now()).select_related('blog')
Entry.objects.select_related('blog').filter(pub_date__gt=timezone.now())

You can follow foreign keys in a similar way to querying them. If you have the following models:

from django.db import models

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

class Person(models.Model):
    # ...
    hometown = models.ForeignKey(
        City,
        on_delete=models.SET_NULL,
        blank=True,
        null=True,
    )

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

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

# Hits the database with joins to the author and hometown tables.
b = Book.objects.select_related('author__hometown').get(id=4)
p = b.author         # Doesn't hit the database.
c = p.hometown       # Doesn't hit the database.

# Without select_related()...
b = Book.objects.get(id=4)  # Hits the database.
p = b.author         # Hits the database.
c = p.hometown       # Hits the database.

You can refer to any ForeignKey or OneToOneField relation in the list of fields passed to select_related().

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

There may be some situations where you wish to call select_related() with a lot of related objects, or where you don’t know all of the relations. In these cases it is possible to call select_related() with no arguments. This will follow all non-null foreign keys it can find - nullable foreign keys must be specified. This is not recommended in most cases as it is likely to make the underlying query more complex, and return more data, than is actually needed.

If you need to clear the list of related fields added by past calls of select_related on a QuerySet, you can pass None as a parameter:

>>> without_relations = queryset.select_related(None)

Chaining select_related calls works in a similar way to other methods - that is that select_related('foo', 'bar') is equivalent to select_related('foo').select_related('bar').

prefetch_related()¶

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 an 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 GenericRelation and GenericForeignKey, however, it must be restricted to a homogeneous set of results. For example, prefetching objects referenced by a GenericForeignKey is only supported if the query is restricted to one ContentType.

For example, suppose you have these models:

from django.db import 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 __str__(self):
        return "%s (%s)" % (
            self.name,
            ", ".join(topping.name for topping in self.toppings.all()),
        )

and run:

>>> Pizza.objects.all()
["Hawaiian (ham, pineapple)", "Seafood (prawns, smoked salmon)"...

The problem with this is that every time Pizza.__str__() asks for self.toppings.all() it has to query the database, so Pizza.objects.all() will run a query on the Toppings table for every item in the Pizza QuerySet.

We can reduce to just two queries using prefetch_related:

>>> Pizza.objects.prefetch_related('toppings')

This implies a self.toppings.all() for each Pizza; now each time self.toppings.all() is called, instead of having to go to the database for the items, it will find them in a prefetched QuerySet cache that was populated in a single query.

That is, all the relevant toppings will have been 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 in prefetch_related() are executed after the QuerySet has begun to be evaluated and the primary query has been executed.

If you have an iterable of model instances, you can prefetch related attributes on those instances using the prefetch_related_objects() function.

Note that the result cache of the primary QuerySet and all specified related objects will then be fully loaded into memory. This changes the typical behavior of QuerySets, which normally try to avoid loading all objects into memory before they are needed, even after a query has been executed in the database.

>>> pizzas = Pizza.objects.prefetch_related('toppings')
>>> [list(pizza.toppings.filter(spicy=True)) for pizza in pizzas]

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.ManyToManyField(Pizza, related_name='restaurants')
    best_pizza = models.ForeignKey(Pizza, related_name='championed_by', on_delete=models.CASCADE)

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 the toppings.

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 an 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!

You can use the Prefetch object to further control the prefetch operation.

In its simplest form Prefetch is equivalent to the traditional string based lookups:

>>> from django.db.models import Prefetch
>>> Restaurant.objects.prefetch_related(Prefetch('pizzas__toppings'))

You can provide a custom queryset with the optional queryset argument. This can be used to change the default ordering of the queryset:

>>> Restaurant.objects.prefetch_related(
...     Prefetch('pizzas__toppings', queryset=Toppings.objects.order_by('name')))

Or to call select_related() when applicable to reduce the number of queries even further:

>>> Pizza.objects.prefetch_related(
...     Prefetch('restaurants', queryset=Restaurant.objects.select_related('best_pizza')))

You can also assign the prefetched result to a custom attribute with the optional to_attr argument. The result will be stored directly in a list.

This allows prefetching the same relation multiple times with a different QuerySet; for instance:

>>> vegetarian_pizzas = Pizza.objects.filter(vegetarian=True)
>>> Restaurant.objects.prefetch_related(
...     Prefetch('pizzas', to_attr='menu'),
...     Prefetch('pizzas', queryset=vegetarian_pizzas, to_attr='vegetarian_menu'))

Lookups created with custom to_attr can still be traversed as usual by other lookups:

>>> vegetarian_pizzas = Pizza.objects.filter(vegetarian=True)
>>> Restaurant.objects.prefetch_related(
...     Prefetch('pizzas', queryset=vegetarian_pizzas, to_attr='vegetarian_menu'),
...     'vegetarian_menu__toppings')

Using to_attr is recommended when filtering down the prefetch result as it is less ambiguous than storing a filtered result in the related manager’s cache:

>>> queryset = Pizza.objects.filter(vegetarian=True)
>>>
>>> # Recommended:
>>> restaurants = Restaurant.objects.prefetch_related(
...     Prefetch('pizzas', queryset=queryset, to_attr='vegetarian_pizzas'))
>>> vegetarian_pizzas = restaurants[0].vegetarian_pizzas
>>>
>>> # Not recommended:
>>> restaurants = Restaurant.objects.prefetch_related(
...     Prefetch('pizzas', queryset=queryset))
>>> vegetarian_pizzas = restaurants[0].pizzas.all()

Custom prefetching also works with single related relations like forward ForeignKey or OneToOneField. Generally you’ll want to use select_related() for these relations, but there are a number of cases where prefetching with a custom QuerySet is useful:

• You want to use a QuerySet that performs further prefetching on related models.

• You want to prefetch only a subset of the related objects.

• You want to use performance optimization techniques like deferred fields:

  >>> queryset = Pizza.objects.only('name')
  >>>
  >>> restaurants = Restaurant.objects.prefetch_related(
  ...     Prefetch('best_pizza', queryset=queryset))
  

When using multiple databases, Prefetch will respect your choice of database. If the inner query does not specify a database, it will use the database selected by the outer query. All of the following are valid:

>>> # Both inner and outer queries will use the 'replica' database
>>> Restaurant.objects.prefetch_related('pizzas__toppings').using('replica')
>>> Restaurant.objects.prefetch_related(
...     Prefetch('pizzas__toppings'),
... ).using('replica')
>>>
>>> # Inner will use the 'replica' database; outer will use 'default' database
>>> Restaurant.objects.prefetch_related(
...     Prefetch('pizzas__toppings', queryset=Toppings.objects.using('replica')),
... )
>>>
>>> # Inner will use 'replica' database; outer will use 'cold-storage' database
>>> Restaurant.objects.prefetch_related(
...     Prefetch('pizzas__toppings', queryset=Toppings.objects.using('replica')),
... ).using('cold-storage')

>>> prefetch_related('pizzas__toppings', 'pizzas')

>>> prefetch_related('pizzas__toppings', Prefetch('pizzas', queryset=Pizza.objects.all()))

>>> prefetch_related('pizza_list__toppings', Prefetch('pizzas', to_attr='pizza_list'))

extra()¶

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.

>>> qs.extra(
...     select={'val': "select col from sometable where othercol = %s"},
...     select_params=(someparam,),
... )

>>> qs.annotate(val=RawSQL("select col from sometable where othercol = %s", (someparam,)))

SELECT col FROM sometable WHERE othercol = '%s'  # unsafe!

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.

  This will work, for example:

  Blog.objects.extra(
      select={'a': '%s', 'b': '%s'},
      select_params=('one', 'two'),
  )
  

  If you need to use a literal %s inside your select string, use the sequence %%s.

• 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=["foo='a' OR bar = 'a'", "baz = 'a'"])
  

  …translates (roughly) into the following SQL:

  SELECT * FROM blog_entry WHERE (foo='a' OR bar='a') AND (baz='a')
  

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

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

• order_by

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

  For example:

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

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

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

• params

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

  Example:

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

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

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

  Good:

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

defer()¶

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 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 select_related()) by using the standard double-underscore notation to separate related fields:

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

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

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

Some fields in a model won’t be deferred, even if you ask for them. You can never defer the loading of the primary key. If you are using select_related() to retrieve related models, you shouldn’t defer the loading of the field that connects from the primary model to the related one, doing so will result in an error.

class CommonlyUsedModel(models.Model):
    f1 = models.CharField(max_length=10)

    class Meta:
        managed = False
        db_table = 'app_largetable'

class ManagedModel(models.Model):
    f1 = models.CharField(max_length=10)
    f2 = models.CharField(max_length=10)

    class Meta:
        db_table = 'app_largetable'

# Two equivalent QuerySets:
CommonlyUsedModel.objects.all()
ManagedModel.objects.defer('f2')

only()¶

The only() method is more or less the opposite of defer(). You call it with the fields that should not be deferred when retrieving a model. If you have a model where almost all the fields need to be deferred, using only() to specify the complementary set of fields 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 defer() documentation apply to only() as well. Use it cautiously and only after exhausting your other options.

Using only() and omitting a field requested using select_related() is an error as well.

As with defer(), you cannot access the non-loaded fields from asynchronous code and expect them to load. Instead, you will get a SynchronousOnlyOperation exception. Ensure that all fields you might access are in your only() call.

using()¶

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 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()¶

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:

from django.db import transaction

entries = Entry.objects.select_for_update().filter(author=request.user)
with transaction.atomic():
    for entry in entries:
        ...

When the queryset is evaluated (for entry in entries in this case), 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, DatabaseError will be raised when the queryset is evaluated. You can also ignore locked rows by using select_for_update(skip_locked=True) instead. The nowait and skip_locked are mutually exclusive and attempts to call select_for_update() with both options enabled will result in a ValueError.

By default, select_for_update() locks all rows that are selected by the query. For example, rows of related objects specified in select_related() are locked in addition to rows of the queryset’s model. If this isn’t desired, specify the related objects you want to lock in select_for_update(of=(...)) using the same fields syntax as select_related(). Use the value 'self' to refer to the queryset’s model.

Restaurant.objects.select_for_update(of=('self', 'place_ptr'))

On PostgreSQL only, you can pass no_key=True in order to acquire a weaker lock, that still allows creating rows that merely reference locked rows (through a foreign key, for example) while the lock is in place. The PostgreSQL documentation has more details about row-level lock modes.

You can’t use select_for_update() on nullable relations:

>>> Person.objects.select_related('hometown').select_for_update()
Traceback (most recent call last):
...
django.db.utils.NotSupportedError: FOR UPDATE cannot be applied to the nullable side of an outer join

To avoid that restriction, you can exclude null objects if you don’t care about them:

>>> Person.objects.select_related('hometown').select_for_update().exclude(hometown=None)
<QuerySet [<Person: ...)>, ...]>

The postgresql, oracle, and mysql database backends support select_for_update(). However, MariaDB only supports the nowait argument, MariaDB 10.6+ also supports the skip_locked argument, and MySQL 8.0.1+ supports the nowait, skip_locked, and of arguments. The no_key argument is only supported on PostgreSQL.

Passing nowait=True, skip_locked=True, no_key=True, or of to select_for_update() using database backends that do not support these options, such as MySQL, raises a NotSupportedError. This prevents code from unexpectedly blocking.

Evaluating a queryset with select_for_update() in autocommit mode on backends which support SELECT ... FOR UPDATE is a TransactionManagementError error because the rows are not locked in that case. If allowed, this would facilitate data corruption and could easily be caused by calling code that expects to be run in a transaction outside of one.

Using select_for_update() on backends which do not support SELECT ... FOR UPDATE (such as SQLite) will have no effect. SELECT ... FOR UPDATE will not be added to the query, and an error isn’t raised if select_for_update() is used in autocommit mode.

raw()¶

Takes a raw SQL query, executes it, and returns a django.db.models.query.RawQuerySet instance. This RawQuerySet instance can be iterated over just like a normal QuerySet to provide object instances.

See the Performing raw SQL queries for more information.