QuerySet API reference
This document describes the details of the QuerySet
API. It builds on the material presented in the model and 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 example Weblog models presented in the database query guide.
When QuerySet
s 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)
Note: Don’t use this if all you want to do is determine if at least one result exists. It’s more efficient to use
exists()
. -
Slicing. As explained in Limiting QuerySets, a
QuerySet
can be sliced, using Python’s array-slicing syntax. Slicing an unevaluatedQuerySet
usually returns another unevaluatedQuerySet
, but Django will execute the database query if you use the “step” parameter of slice syntax, and will return a list. Slicing aQuerySet
that has been evaluated also returns a list.Also note that even though slicing an unevaluated
QuerySet
returns another unevaluatedQuerySet
, modifying it further (e.g., adding more filters, or modifying ordering) is not allowed, since that does not translate well into SQL and it would not have a clear meaning either. - 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 callrepr()
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 calllen()
on it. This, as you might expect, returns the length of the result list.Note: If you only need to determine the number of records in the set (and don’t need the actual objects), it’s much more efficient to handle a count at the database level using SQL’s
SELECT COUNT(*)
. Django provides acount()
method for precisely this reason. -
list(). Force evaluation of a
QuerySet
by callinglist()
on it. For example:entry_list = list(Entry.objects.all())
-
bool(). Testing a
QuerySet
in a boolean context, such as usingbool()
,or
,and
or anif
statement, will cause the query to be executed. If there is at least one result, theQuerySet
isTrue
, otherwiseFalse
. For example:if Entry.objects.filter(headline="Test"): print("There is at least one Entry with the headline Test")
Note: If you only want to determine if at least one result exists (and don’t need the actual objects), it’s more efficient to use
exists()
.
Pickling QuerySet
s
If you pickle
a QuerySet
, this will force all the results to be loaded into memory prior to pickling. Pickling is usually used as a precursor to caching and when the cached queryset is reloaded, you want the results to already be present and ready for use (reading from the database can take some time, defeating the purpose of caching). This means that when you unpickle a QuerySet
, it contains the results at the moment it was pickled, rather than the results that are currently in the database.
If you only want to pickle the necessary information to recreate the QuerySet
from the database at a later time, pickle the query
attribute of the QuerySet
. You can then recreate the original QuerySet
(without any results loaded) using some code like this:
>>> import pickle >>> query = pickle.loads(s) # Assuming 's' is the pickled string. >>> qs = MyModel.objects.all() >>> qs.query = query # Restore the original 'query'.
The query
attribute is an opaque object. It represents the internals of the query construction and is not part of the public API. However, it is safe (and fully supported) to pickle and unpickle the attribute’s contents as described here.
QuerySet
API
Here’s the formal declaration of a QuerySet
:
-
class QuerySet(model=None, query=None, using=None)
[source] -
Usually when you’ll interact with a
QuerySet
you’ll use it by chaining filters. To make this work, mostQuerySet
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:-
ordered
-
True
if theQuerySet
is ordered — i.e. has anorder_by()
clause or a default ordering on the model.False
otherwise.
-
db
-
The database that will be used if this query is executed now.
Note
The
query
parameter toQuerySet
exists so that specialized query subclasses such asGeoQuerySet
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. -
Methods that return new QuerySet
s
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()
-
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.
If you need to execute more complex queries (for example, queries with OR
statements), you can use Q objects
.
exclude()
-
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.
If you need to execute more complex queries (for example, queries with OR
statements), you can use Q objects
.
annotate()
-
annotate(*args, **kwargs)
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
.
Previous versions of Django only allowed aggregate functions to be used as annotations. It is now possible to annotate a model with all kinds of expressions.
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.
order_by()
-
order_by(*fields)
By default, results returned by a QuerySet
are ordered by the ordering tuple given by the ordering
option in the model’s Meta
. You can override this on a per-QuerySet
basis by using the order_by
method.
Example:
Entry.objects.filter(pub_date__year=2005).order_by('-pub_date', 'headline')
The result above will be ordered by pub_date
descending, then by headline
ascending. The negative sign in front of "-pub_date"
indicates descending order. Ascending order is implied. To order randomly, use "?"
, like so:
Entry.objects.order_by('?')
Note: order_by('?')
queries may be expensive and slow, depending on the database backend you’re using.
To order by a field in a different model, use the same syntax as when you are querying across model relations. That is, the name of the field, followed by a double underscore (__
), followed by the name of the field in the new model, and so on for as many models as you want to join. For example:
Entry.objects.order_by('blog__name', 'headline')
If you try to order by a field that is a relation to another model, Django will use the default ordering on the related model, or order by the related model’s primary key if there is no Meta.ordering
specified. For example, 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')
It is also possible to order a queryset by a related field, without incurring the cost of a JOIN, by referring to the _id
of the related field:
# No Join Entry.objects.order_by('blog_id') # Join Entry.objects.order_by('blog__id')
You can also order by query expressions by calling asc()
or desc()
on the expression:
Entry.objects.order_by(Coalesce('summary', 'headline').desc())
Ordering by query expressions was added.
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.
Note
It is permissible to specify a multi-valued field to order the results by (for example, a ManyToManyField
field, or the reverse relation of a ForeignKey
field).
Consider this case:
class Event(Model): parent = models.ForeignKey( 'self', on_delete=models.CASCADE, related_name='children', ) date = models.DateField() Event.objects.order_by('children__date')
Here, there could potentially be multiple ordering data for each Event
; each Event
with multiple children
will be returned multiple times into the new QuerySet
that order_by()
creates. In other words, using order_by()
on the QuerySet
could return more items than you were working on to begin with - which is probably neither expected nor useful.
Thus, take care when using multi-valued field to order the results. If you can be sure that there will only be one ordering piece of data for each of the items you’re ordering, this approach should not present problems. If not, 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.
You can order by a field converted to lowercase with Lower
which will achieve case-consistent ordering:
Entry.objects.order_by(Lower('headline').desc())
The ability to order by expressions like Lower
was added.
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')
Warning
Ordering is not a free operation. Each field you add to the ordering incurs a cost to your database. Each foreign key you add will implicitly include all of its default orderings as well.
reverse()
-
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()
-
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 order_by()
call are included in the SQL SELECT
columns. This can sometimes lead to unexpected results when used in conjunction with distinct()
. If you order by fields from a related model, those fields will be added to the selected columns and they may make otherwise duplicate rows appear to be distinct. Since the extra columns don’t appear in the returned results (they are only there to support ordering), it sometimes looks like non-distinct results are being returned.
Similarly, if you use a values()
query to restrict the columns selected, the columns used in any order_by()
(or default model ordering) will still be involved and may affect uniqueness of the results.
The moral here is that if you are using distinct()
be careful about ordering by related models. Similarly, when using distinct()
and values()
together, be careful when ordering by fields not in the values()
call.
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.
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 (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') [...]
Note
Keep in mind that order_by()
uses any default related model ordering that has been defined. You might have to explicitly order by the relation _id
or referenced field to make sure the DISTINCT ON
expressions match those at the beginning of the ORDER BY
clause. For example, if the Blog
model defined an ordering
by name
:
Entry.objects.order_by('blog').distinct('blog')
...wouldn’t work because the query would be ordered by blog__name
thus mismatching the DISTINCT ON
expression. You’d have to explicitly order by the relation _id
field (blog_id
in this case) or the referenced one (blog__pk
) to make sure both expressions match.
values()
-
values(*fields)
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') [<Blog: Beatles Blog>] # This list contains a dictionary. >>> Blog.objects.filter(name__startswith='Beatles').values() [{'id': 1, 'name': 'Beatles Blog', 'tagline': 'All the latest Beatles news.'}]
The values()
method takes optional positional arguments, *fields
, which specify field names to which the SELECT
should be limited. If you specify the fields, each dictionary will contain only the field keys/values for the fields you specify. If you don’t specify the fields, each dictionary will contain a key and value for every field in the database table.
Example:
>>> Blog.objects.values() [{'id': 1, 'name': 'Beatles Blog', 'tagline': 'All the latest Beatles news.'}], >>> Blog.objects.values('id', 'name') [{'id': 1, 'name': 'Beatles Blog'}]
A few subtleties that are worth mentioning:
-
If you have a field called
foo
that is aForeignKey
, the defaultvalues()
call will return a dictionary key calledfoo_id
, since this is the name of the hidden model attribute that stores the actual value (thefoo
attribute refers to the related model). When you are callingvalues()
and passing in field names, you can pass in eitherfoo
orfoo_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': 'First Entry', ...}, ...] >>> Entry.objects.values('blog') [{'blog': 1}, ...] >>> Entry.objects.values('blog_id') [{'blog_id': 1}, ...]
- When using
values()
together withdistinct()
, be aware that ordering can affect the results. See the note indistinct()
for details. - If you use a
values()
clause after anextra()
call, any fields defined by aselect
argument in theextra()
must be explicitly included in thevalues()
call. Anyextra()
call made after avalues()
call will have its extra selected fields ignored. - Calling
only()
anddefer()
aftervalues()
doesn’t make sense, so doing so will raise aNotImplementedError
.
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') [{'name': 'My blog', 'entry__headline': 'An entry'}, {'name': 'My blog', 'entry__headline': 'Another entry'}, ...]
Warning
Because 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()
-
values_list(*fields, flat=False)
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, '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.
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') [('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') [('Noam Chomsky',), ('George Orwell',), (None,)]
dates()
-
dates(field, kind, order='ASC')
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"
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. -
"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', '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()
-
datetimes(field_name, kind, order='ASC', tzinfo=None)
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"
, "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
.
Note
This function performs time zone conversions directly in the database. As a consequence, your database must be able to interpret the value of tzinfo.tzname(None)
. This translates into the following requirements:
- SQLite: install pytz — conversions are actually performed in Python.
- PostgreSQL: no requirements (see Time Zones).
- Oracle: no requirements (see Choosing a Time Zone File).
- MySQL: install pytz and load the time zone tables with mysql_tzinfo_to_sql.
none()
-
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() [] >>> from django.db.models.query import EmptyQuerySet >>> isinstance(Entry.objects.none(), EmptyQuerySet) True
all()
-
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
.
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
:
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. b = Book.objects.get(id=4) # No select_related() in this example. 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): # __unicode__ on Python 2 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.all().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.
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.
Note
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. The prefetch_related('toppings')
implied pizza.toppings.all()
, but pizza.toppings.filter()
is a new and different query. The prefetched cache can’t help here; 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.ManyToManyField(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 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!
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.
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:
>>> 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))
Note
The ordering of lookups matters.
Take the following examples:
>>> prefetch_related('pizzas__toppings', 'pizzas')
This works even though it’s unordered because 'pizzas__toppings'
already contains all the needed information, therefore the second argument 'pizzas'
is actually redundant.
>>> prefetch_related('pizzas__toppings', Prefetch('pizzas', queryset=Pizza.objects.all()))
This will raise a ValueError
because of the attempt to redefine the queryset of a previously seen lookup. Note that an implicit queryset was created to traverse 'pizzas'
as part of the 'pizzas__toppings'
lookup.
>>> prefetch_related('pizza_list__toppings', Prefetch('pizzas', to_attr='pizza_list'))
This will trigger an AttributeError
because 'pizza_list'
doesn’t exist yet when 'pizza_list__toppings'
is being processed.
This consideration is not limited to the use of Prefetch
objects. Some advanced techniques may require that the lookups be performed in a specific order to avoid creating extra queries; therefore it’s recommended to always carefully order prefetch_related
arguments.
extra()
-
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
.
Use this method as a last resort
This is an old API that we aim to deprecate at some point in the future. Use it only if you cannot express your query using other queryset methods. If you do need to use it, please file a ticket using the QuerySet.extra keyword with your use case (please check the list of existing tickets first) so that we can enhance the QuerySet API to allow removing extra()
. We are no longer improving or fixing bugs for this method.
For example, this use of extra()
:
>>> qs.extra( ... select={'val': "select col from sometable where othercol = %s"}, ... select_params=(someparam,), ... )
is equivalent to:
>>> qs.annotate(val=RawSQL("select col from sometable where othercol = %s", (someparam,)))
The main benefit of using RawSQL
is that you can set output_field
if needed. The main downside is that if you refer to some table alias of the queryset in the raw SQL, then it is possible that Django might change that alias (for example, when the queryset is used as a subquery in yet another query).
Warning
You should be very careful whenever you use extra()
. Every time you use it, you should escape any parameters that the user can control by using params
in order to protect against SQL injection attacks . Please read more about SQL injection protection.
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 theSELECT
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’spub_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 anentry_count
attribute, an integer count of associatedEntry
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 itsFROM
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 theselect_params
parameter. Sinceselect_params
is a sequence and theselect
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 acollections.OrderedDict
for theselect
value, not just a normal Python dictionary.This will work, for example:
Blog.objects.extra( select=OrderedDict([('a', '%s'), ('b', '%s')]), select_params=('one', 'two'))
If you need to use a literal
%s
inside your select string, use the sequence%%s
.Prior to 1.8, you were unable to escape a literal
%s
. -
where
/tables
You can define explicit SQL
WHERE
clauses — perhaps to perform non-explicit joins — by usingwhere
. You can manually add tables to the SQLFROM
clause by usingtables
.where
andtables
both take a list of strings. Allwhere
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 thetables
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 extrawhere
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 yourwhere
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 theorder_by
parameter toextra()
and pass in a sequence of strings. These strings should either be model fields (as in the normalorder_by()
method on querysets), of the formtable_name.column_name
or an alias for a column that you specified in theselect
parameter toextra()
.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 beforeFalse
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. Theparams
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 intowhere
becauseparams
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'])
Warning
If you are performing queries on MySQL, note that MySQL’s silent type coercion may cause unexpected results when mixing types. If you query on a string type column, but with an integer value, MySQL will coerce the types of all values in the table to an integer before performing the comparison. For example, if your table contains the values 'abc'
, 'def'
and you query for WHERE mycolumn=0
, both rows will match. To prevent this, perform the correct typecasting before using the value in a query.
defer()
-
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 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.
Note
The defer()
method (and its cousin, only()
, below) are only for advanced use-cases. They provide an optimization for when you have analyzed your queries closely and understand exactly what information you need and have measured that the difference between returning the fields you need and the full set of fields for the model will be significant.
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 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.
For example, both of these models use the same underlying database table:
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.all().defer('f2')
If many fields need to be duplicated in the unmanaged model, it may be best to create an abstract model with the shared fields and then have the unmanaged and managed models inherit from the abstract model.
Note
When calling save()
for instances with deferred fields, only the loaded fields will be saved. See save()
for more details.
only()
-
only(*fields)
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.
Note
When calling save()
for instances with deferred fields, only the loaded fields will be saved. See save()
for more details.
using()
-
using(alias)
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()
-
select_for_update(nowait=False)
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, DatabaseError
will be raised when the queryset is evaluated.
Currently, the postgresql
, 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 DatabaseError
to be raised. This is in order to prevent code 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.
Warning
Although select_for_update()
normally fails in autocommit mode, since TestCase
automatically wraps each test in a transaction, calling select_for_update()
in a TestCase
even outside an atomic()
block will (perhaps unexpectedly) pass without raising a TransactionManagementError
. To properly test select_for_update()
you should use TransactionTestCase
.
raw()
-
raw(raw_query, params=None, translations=None)
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 an normal QuerySet
to provide object instances.
See the Performing raw SQL queries for more information.
Warning
raw()
always triggers a new query and doesn’t account for previous filtering. As such, it should generally be called from the Manager
or from a fresh QuerySet
instance.
Methods that do not return QuerySet
s
The following QuerySet
methods evaluate the QuerySet
and return something other than a QuerySet
.
These methods do not use a cache (see Caching and QuerySets). Rather, they query the database each time they’re called.
get()
-
get(**kwargs)
Returns the object matching the given lookup parameters, which should be in the format described in Field lookups.
get()
raises MultipleObjectsReturned
if more than one object was found. The MultipleObjectsReturned
exception is an attribute of the model class.
get()
raises a DoesNotExist
exception if an object wasn’t found for the given parameters. This exception is an attribute of the model class. Example:
Entry.objects.get(id='foo') # raises Entry.DoesNotExist
The DoesNotExist
exception inherits from django.core.exceptions.ObjectDoesNotExist
, so you can target multiple DoesNotExist
exceptions. Example:
from django.core.exceptions import ObjectDoesNotExist try: e = Entry.objects.get(id=3) b = Blog.objects.get(id=1) except ObjectDoesNotExist: print("Either the entry or blog doesn't exist.")
create()
-
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 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 IntegrityError
since primary keys must be unique. Be prepared to handle the exception if you are using manual primary keys.
get_or_create()
-
get_or_create(defaults=None, **kwargs)
A convenience method for looking up an object with the given kwargs
(may be empty if your model has defaults for all fields), 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. For example:
try: obj = Person.objects.get(first_name='John', last_name='Lennon') except Person.DoesNotExist: obj = Person(first_name='John', last_name='Lennon', birthday=date(1940, 10, 9)) obj.save()
This pattern gets quite unwieldy as the number of fields in a model goes up. The above example can be rewritten using get_or_create()
like so:
obj, created = Person.objects.get_or_create( first_name='John', last_name='Lennon', defaults={'birthday': date(1940, 10, 9)}, )
Any keyword arguments passed to get_or_create()
— except an optional one called defaults
— will be used in a get()
call. If an object is found, get_or_create()
returns a tuple of that object and False
. If multiple objects are found, get_or_create
raises MultipleObjectsReturned
. 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:
params = {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 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 IntegrityError
will be raised.
This method is atomic assuming correct usage, correct database configuration, and correct behavior of the underlying database. However, if uniqueness is not enforced at the database level for the kwargs
used in a get_or_create
call (see unique
or unique_together
), this method is prone to a race-condition which can result in multiple rows with the same parameters being inserted simultaneously.
If you are using MySQL, be sure to use the READ COMMITTED
isolation level rather than REPEATABLE READ
(the default), otherwise you may see cases where get_or_create
will raise an IntegrityError
but the object won’t appear in a subsequent get()
call.
Finally, a word on using get_or_create()
in Django views. 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. Instead, use POST
whenever a request to a page has a side effect on your data. For more, see Safe methods in the HTTP spec.
Warning
You can use get_or_create()
through ManyToManyField
attributes and reverse relations. In that case you will restrict the queries inside the context of that relation. That could lead you to some integrity problems if you don’t use it consistently.
Being the following models:
class Chapter(models.Model): title = models.CharField(max_length=255, unique=True) class Book(models.Model): title = models.CharField(max_length=256) chapters = models.ManyToManyField(Chapter)
You can use get_or_create()
through Book’s chapters field, but it only fetches inside the context of that book:
>>> book = Book.objects.create(title="Ulysses") >>> book.chapters.get_or_create(title="Telemachus") (<Chapter: Telemachus>, True) >>> book.chapters.get_or_create(title="Telemachus") (<Chapter: Telemachus>, False) >>> Chapter.objects.create(title="Chapter 1") <Chapter: Chapter 1> >>> book.chapters.get_or_create(title="Chapter 1") # Raises IntegrityError
This is happening because it’s trying to get or create “Chapter 1” through the book “Ulysses”, but it can’t do any of them: the relation can’t fetch that chapter because it isn’t related to that book, but it can’t create it either because title
field should be unique.
update_or_create()
-
update_or_create(defaults=None, **kwargs)
A convenience method for updating an object with the given kwargs
, creating a new one if necessary. The defaults
is a dictionary of (field, value) pairs used to update the object.
Returns a tuple of (object, created)
, where object
is the created or updated object and created
is a boolean specifying whether a new object was created.
The update_or_create
method tries to fetch an object from database based on the given kwargs
. If a match is found, it updates the fields passed in the defaults
dictionary.
This is meant as a shortcut to boilerplatish code. For example:
defaults = {'first_name': 'Bob'} try: obj = Person.objects.get(first_name='John', last_name='Lennon') for key, value in defaults.items(): setattr(obj, key, value) obj.save() except Person.DoesNotExist: new_values = {'first_name': 'John', 'last_name': 'Lennon'} new_values.update(defaults) obj = Person(**new_values) obj.save()
This pattern gets quite unwieldy as the number of fields in a model goes up. The above example can be rewritten using update_or_create()
like so:
obj, created = Person.objects.update_or_create( first_name='John', last_name='Lennon', defaults={'first_name': 'Bob'}, )
For detailed description how names passed in kwargs
are resolved see get_or_create()
.
As described above in get_or_create()
, this method is prone to a race-condition which can result in multiple rows being inserted simultaneously if uniqueness is not enforced at the database level.
bulk_create()
-
bulk_create(objs, batch_size=None)
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 thepre_save
andpost_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
AutoField
it does not retrieve and set the primary key attribute, assave()
does. - It does not work with many-to-many relationships.
Support for using bulk_create()
with proxy models was added.
The batch_size
parameter controls how many objects are created in single query. The default is to create all objects in one batch, except for SQLite where the default is such that at most 999 variables per query are used.
count()
-
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.
Note that if you want the number of items in a QuerySet
and are also retrieving model instances from it (for example, by iterating over it), it’s probably more efficient to use len(queryset)
which won’t cause an extra database query like count()
would.
in_bulk()
-
in_bulk(id_list)
Takes a list of primary-key values and returns a dictionary mapping each primary-key value to an instance of the object with the given ID.
Example:
>>> Blog.objects.in_bulk([1]) {1: <Blog: Beatles Blog>} >>> Blog.objects.in_bulk([1, 2]) {1: <Blog: Beatles Blog>, 2: <Blog: Cheddar Talk>} >>> Blog.objects.in_bulk([]) {}
If you pass in_bulk()
an empty list, you’ll get an empty dictionary.
iterator()
-
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 result 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.
Warning
Some Python database drivers like psycopg2
perform caching if using client side cursors (instantiated with connection.cursor()
and what Django’s ORM uses). Using iterator()
does not affect caching at the database driver level. To disable this caching, look at server side cursors.
latest()
-
latest(field_name=None)
Returns the latest object in the table, by date, using the field_name
provided as the date field.
This example returns the latest Entry
in the table, according to the pub_date
field:
Entry.objects.latest('pub_date')
If your model’s Meta specifies get_latest_by
, you can leave off the field_name
argument to earliest()
or latest()
. Django will use the field specified in get_latest_by
by default.
Like get()
, earliest()
and latest()
raise DoesNotExist
if there is no object with the given parameters.
Note that earliest()
and latest()
exist purely for convenience and readability.
earliest()
and latest()
may return instances with null dates.
Since ordering is delegated to the database, results on fields that allow null values may be ordered differently if you use different databases. For example, PostgreSQL and MySQL sort null values as if they are higher than non-null values, while SQLite does the opposite.
You may want to filter out null values:
Entry.objects.filter(pub_date__isnull=False).latest('pub_date')
earliest()
-
earliest(field_name=None)
Works otherwise like latest()
except the direction is changed.
first()
-
first()
Returns the first object matched by the queryset, or None
if there is no matching object. If the QuerySet
has no ordering defined, then the queryset is automatically ordered by the primary key.
Example:
p = Article.objects.order_by('title', 'pub_date').first()
Note that first()
is a convenience method, the following code sample is equivalent to the above example:
try: p = Article.objects.order_by('title', 'pub_date')[0] except IndexError: p = None
last()
-
last()
Works like first()
, but returns the last object in the queryset.
aggregate()
-
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. Since aggregates are also query expressions, you may combine aggregates with other aggregates or values to create complex aggregates.
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. Complex aggregates cannot use anonymous arguments and must specify a keyword argument as an alias.
For example, when you are working with blog entries, you may want to know the number of authors that have contributed blog entries:
>>> from django.db.models import Count >>> 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 the topic guide on Aggregation.
exists()
-
exists()
Returns True
if the 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 as a normal QuerySet
query.
exists()
is useful for searches relating to both object membership in a QuerySet
and to the existence of any objects in a QuerySet
, particularly in the context of a large QuerySet
.
The most efficient method of finding whether a model with a unique field (e.g. primary_key
) is a member of a QuerySet
is:
entry = Entry.objects.get(pk=123) if some_queryset.filter(pk=entry.pk).exists(): print("Entry contained in queryset")
Which will be faster than the following which requires evaluating and iterating through the entire queryset:
if entry in some_queryset: print("Entry contained in QuerySet")
And to find whether a queryset contains any items:
if some_queryset.exists(): print("There is at least one object in some_queryset")
Which will be faster than:
if some_queryset: print("There is at least one object in some_queryset")
... but not by a large degree (hence needing a large queryset for efficiency gains).
Additionally, if a some_queryset
has not yet been evaluated, but you know that it will be at some point, then using some_queryset.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_queryset)
, which retrieves the results and then checks if any were returned.
update()
-
update(**kwargs)
Performs an SQL update query for the specified fields, and returns the number of rows matched (which may not be equal to the number of rows updated if some rows already have the new value).
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 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 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 pre_save
or post_save
signals (which are a consequence of calling Model.save()
). If you want to update a bunch of records for a model that has a custom save()
method, loop over them and call save()
, like this:
for e in Entry.objects.filter(pub_date__year=2010): e.comments_on = False e.save()
delete()
-
delete()
Performs an SQL delete query on all rows in the QuerySet
and returns the number of objects deleted and a dictionary with the number of deletions per object type.
The delete()
is applied instantly. You cannot call delete()
on a 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() (4, {'weblog.Entry': 2, 'weblog.Entry_authors': 2})
The return value describing the number of objects deleted was added.
By default, Django’s 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() (5, {'weblog.Blog': 1, 'weblog.Entry': 2, 'weblog.Entry_authors': 2})
This cascade behavior is customizable via the on_delete
argument to the ForeignKey
.
The delete()
method does a bulk delete and does not call any delete()
methods on your models. It does, however, emit the pre_delete
and post_delete
signals for all deleted objects (including cascaded deletions).
Django needs to fetch objects into memory to send signals and handle cascades. However, if there are no cascades and no signals, then Django may take a fast-path and delete objects without fetching into memory. For large deletes this can result in significantly reduced memory usage. The amount of executed queries can be reduced, too.
ForeignKeys which are set to on_delete
DO_NOTHING
do not prevent taking the fast-path in deletion.
Note that the queries generated in object deletion is an implementation detail subject to change.
as_manager()
-
classmethod as_manager()
Class method that returns an instance of Manager
with a copy of the QuerySet
’s methods. See Creating a manager with QuerySet methods for more details.
Field
lookups
Field lookups are how you specify the meat of an SQL WHERE
clause. They’re specified as keyword arguments to the QuerySet
methods filter()
, exclude()
and get()
.
For an introduction, see models and database queries documentation.
Django’s built-in lookups are listed below. It is also possible to write custom lookups for model fields.
As a convenience when no lookup type is provided (like in Entry.objects.get(id=14)
) the lookup type is assumed to be exact
.
exact
Exact match. If the value provided for comparison is None
, it will be interpreted as an SQL NULL
(see isnull
for more details).
Examples:
Entry.objects.get(id__exact=14) Entry.objects.get(id__exact=None)
SQL equivalents:
SELECT ... WHERE id = 14; SELECT ... WHERE id IS NULL;
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 collation section in the databases documentation.
iexact
Case-insensitive exact match. If the value provided for comparison is None
, it will be interpreted as an SQL NULL
(see isnull
for more details).
Example:
Blog.objects.get(name__iexact='beatles blog') Blog.objects.get(name__iexact=None)
SQL equivalents:
SELECT ... WHERE name ILIKE 'beatles blog'; SELECT ... WHERE name IS NULL;
Note the first query will match 'Beatles Blog'
, 'beatles blog'
, 'BeAtLes BLoG'
, etc.
SQLite users
When using the SQLite backend and Unicode (non-ASCII) strings, bear in mind the database note about string comparisons. SQLite does not do case-insensitive matching for Unicode strings.
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'
.
SQLite users
SQLite doesn’t support case-sensitive LIKE
statements; contains
acts like icontains
for SQLite. See the database note for more information.
icontains
Case-insensitive containment test.
Example:
Entry.objects.get(headline__icontains='Lennon')
SQL equivalent:
SELECT ... WHERE headline ILIKE '%Lennon%';
SQLite users
When using the SQLite backend and Unicode (non-ASCII) strings, bear in mind the database note about string comparisons.
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%')
If you pass in a QuerySet
resulting from values()
or values_list()
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)
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 QuerySets are lazy.
gt
Greater than.
Example:
Entry.objects.filter(id__gt=4)
SQL equivalent:
SELECT ... WHERE id > 4;
gte
Greater than or equal to.
lt
Less than.
lte
Less than or equal to.
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.
istartswith
Case-insensitive starts-with.
Example:
Entry.objects.filter(headline__istartswith='will')
SQL equivalent:
SELECT ... WHERE headline ILIKE 'Will%';
SQLite users
When using the SQLite backend and Unicode (non-ASCII) strings, bear in mind the database note about string comparisons.
endswith
Case-sensitive ends-with.
Example:
Entry.objects.filter(headline__endswith='cats')
SQL equivalent:
SELECT ... WHERE headline LIKE '%cats';
SQLite users
SQLite doesn’t support case-sensitive LIKE
statements; endswith
acts like iendswith
for SQLite. Refer to the database note documentation for more.
iendswith
Case-insensitive ends-with.
Example:
Entry.objects.filter(headline__iendswith='will')
SQL equivalent:
SELECT ... WHERE headline ILIKE '%will'
SQLite users
When using the SQLite backend and Unicode (non-ASCII) strings, bear in mind the database note about string comparisons.
range
Range test (inclusive).
Example:
import datetime 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.
Warning
Filtering a DateTimeField
with dates won’t include items on the last day, because the bounds are interpreted as “0am on the given date”. If pub_date
was a DateTimeField
, the above expression would be turned into this SQL:
SELECT ... WHERE pub_date BETWEEN '2005-01-01 00:00:00' and '2005-03-31 00:00:00';
Generally speaking, you can’t mix dates and datetimes.
date
For datetime fields, casts the value as date. Allows chaining additional field lookups. Takes a date value.
Example:
Entry.objects.filter(pub_date__date=datetime.date(2005, 1, 1)) Entry.objects.filter(pub_date__date__gt=datetime.date(2005, 1, 1))
(No equivalent SQL code fragment is included for this lookup because implementation of the relevant query varies among different database engines.)
When USE_TZ
is True
, fields are converted to the current time zone before filtering.
year
For date and datetime fields, an exact year match. Allows chaining additional field lookups. Takes an integer year.
Example:
Entry.objects.filter(pub_date__year=2005) Entry.objects.filter(pub_date__year__gte=2005)
SQL equivalent:
SELECT ... WHERE pub_date BETWEEN '2005-01-01' AND '2005-12-31'; SELECT ... WHERE pub_date >= '2005-01-01';
(The exact SQL syntax varies for each database engine.)
When USE_TZ
is True
, datetime fields are converted to the current time zone before filtering.
Allowed chaining additional field lookups.
month
For date and datetime fields, an exact month match. Allows chaining additional field lookups. Takes an integer 1 (January) through 12 (December).
Example:
Entry.objects.filter(pub_date__month=12) Entry.objects.filter(pub_date__month__gte=6)
SQL equivalent:
SELECT ... WHERE EXTRACT('month' FROM pub_date) = '12'; SELECT ... WHERE EXTRACT('month' FROM pub_date) >= '6';
(The exact SQL syntax varies for each database engine.)
When USE_TZ
is True
, datetime fields are converted to the current time zone before filtering. This requires time zone definitions in the database.
Allowed chaining additional field lookups.
day
For date and datetime fields, an exact day match. Allows chaining additional field lookups. Takes an integer day.
Example:
Entry.objects.filter(pub_date__day=3) Entry.objects.filter(pub_date__day__gte=3)
SQL equivalent:
SELECT ... WHERE EXTRACT('day' FROM pub_date) = '3'; 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.
When USE_TZ
is True
, datetime fields are converted to the current time zone before filtering. This requires time zone definitions in the database.
Allowed chaining additional field lookups.
week_day
For date and datetime fields, a ‘day of the week’ match. Allows chaining additional field lookups.
Takes an integer value representing the day of week from 1 (Sunday) to 7 (Saturday).
Example:
Entry.objects.filter(pub_date__week_day=2) Entry.objects.filter(pub_date__week_day__gte=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.
When USE_TZ
is True
, datetime fields are converted to the current time zone before filtering. This requires time zone definitions in the database.
Allowed chaining additional field lookups.
hour
For datetime and time fields, an exact hour match. Allows chaining additional field lookups. Takes an integer between 0 and 23.
Example:
Event.objects.filter(timestamp__hour=23) Event.objects.filter(time__hour=5) Event.objects.filter(timestamp__hour__gte=12)
SQL equivalent:
SELECT ... WHERE EXTRACT('hour' FROM timestamp) = '23'; SELECT ... WHERE EXTRACT('hour' FROM time) = '5'; SELECT ... WHERE EXTRACT('hour' FROM timestamp) >= '12';
(The exact SQL syntax varies for each database engine.)
For datetime fields, when USE_TZ
is True
, values are converted to the current time zone before filtering.
Added support for TimeField
on SQLite (other databases supported it as of 1.7).
Allowed chaining additional field lookups.
minute
For datetime and time fields, an exact minute match. Allows chaining additional field lookups. Takes an integer between 0 and 59.
Example:
Event.objects.filter(timestamp__minute=29) Event.objects.filter(time__minute=46) Event.objects.filter(timestamp__minute__gte=29)
SQL equivalent:
SELECT ... WHERE EXTRACT('minute' FROM timestamp) = '29'; SELECT ... WHERE EXTRACT('minute' FROM time) = '46'; SELECT ... WHERE EXTRACT('minute' FROM timestamp) >= '29';
(The exact SQL syntax varies for each database engine.)
For datetime fields, When USE_TZ
is True
, values are converted to the current time zone before filtering.
Added support for TimeField
on SQLite (other databases supported it as of 1.7).
Allowed chaining additional field lookups.
second
For datetime and time fields, an exact second match. Allows chaining additional field lookups. Takes an integer between 0 and 59.
Example:
Event.objects.filter(timestamp__second=31) Event.objects.filter(time__second=2) Event.objects.filter(timestamp__second__gte=31)
SQL equivalent:
SELECT ... WHERE EXTRACT('second' FROM timestamp) = '31'; SELECT ... WHERE EXTRACT('second' FROM time) = '2'; SELECT ... WHERE EXTRACT('second' FROM timestamp) >= '31';
(The exact SQL syntax varies for each database engine.)
For datetime fields, when USE_TZ
is True
, values are converted to the current time zone before filtering.
Added support for TimeField
on SQLite (other databases supported it as of 1.7).
Allowed chaining additional field lookups.
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;
search
A boolean full-text search, taking advantage of full-text indexing. This is like contains
but is significantly faster due to full-text indexing.
Example:
Entry.objects.filter(headline__search="+Django -jazz Python")
SQL equivalent:
SELECT ... WHERE MATCH(tablename, headline) AGAINST (+Django -jazz Python IN BOOLEAN MODE);
Note this is only available in MySQL and requires direct manipulation of the database to add the full-text index. By default Django uses BOOLEAN MODE for full text searches. See the MySQL documentation for additional details.
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.
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
Django provides the following aggregation functions in the django.db.models
module. For details on how to use these aggregate functions, see the topic guide on aggregation. See the Aggregate
documentation to learn how to create your aggregates.
Warning
SQLite can’t handle aggregation on date/time fields out of the box. This is because there are no native date/time fields in SQLite and Django currently emulates these features using a text field. Attempts to use aggregation on date/time fields in SQLite will raise NotImplementedError
.
Note
Aggregation functions return None
when used with an empty QuerySet
. For example, the Sum
aggregation function returns None
instead of 0
if the QuerySet
contains no entries. An exception is Count
, which does return 0
if the QuerySet
is empty.
All aggregates have the following parameters in common:
expression
A string that references a field on the model, or a query expression.
Aggregate functions are now able to reference multiple fields in complex computations.
output_field
An optional argument that represents the model field of the return value
The output_field
argument was added.
Note
When combining multiple field types, Django can only determine the output_field
if all fields are of the same type. Otherwise, you must provide the output_field
yourself.
**extra
Keyword arguments that can provide extra context for the SQL generated by the aggregate.
Avg
-
class Avg(expression, output_field=FloatField(), **extra)
[source] -
Returns the mean value of the given expression, which must be numeric unless you specify a different
output_field
.- Default alias:
<field>__avg
- Return type:
float
(or the type of whateveroutput_field
is specified)
The
output_field
parameter was added to allow aggregating over non-numeric columns, such asDurationField
. - Default alias:
Count
-
class Count(expression, distinct=False, **extra)
[source] -
Returns the number of objects that are related through the provided expression.
- Default alias:
<field>__count
- Return type:
int
Has one optional argument:
-
distinct
-
If
distinct=True
, the count will only include unique instances. This is the SQL equivalent ofCOUNT(DISTINCT <field>)
. The default value isFalse
.
- Default alias:
Max
-
class Max(expression, output_field=None, **extra)
[source] -
Returns the maximum value of the given expression.
- Default alias:
<field>__max
- Return type: same as input field, or
output_field
if supplied
- Default alias:
Min
-
class Min(expression, output_field=None, **extra)
[source] -
Returns the minimum value of the given expression.
- Default alias:
<field>__min
- Return type: same as input field, or
output_field
if supplied
- Default alias:
StdDev
-
class StdDev(expression, sample=False, **extra)
[source] -
Returns the standard deviation of the data in the provided expression.
- Default alias:
<field>__stddev
- Return type:
float
Has one optional argument:
-
sample
-
By default,
StdDev
returns the population standard deviation. However, ifsample=True
, the return value will be the sample standard deviation.
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. - Default alias:
Sum
-
class Sum(expression, output_field=None, **extra)
[source] -
Computes the sum of all values of the given expression.
- Default alias:
<field>__sum
- Return type: same as input field, or
output_field
if supplied
- Default alias:
Variance
-
class Variance(expression, sample=False, **extra)
[source] -
Returns the variance of the data in the provided expression.
- Default alias:
<field>__variance
- Return type:
float
Has one optional argument:
-
sample
-
By default,
Variance
returns the population variance. However, ifsample=True
, the return value will be the sample variance.
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. - Default alias:
Query-related classes
This section provides reference material for query-related tools not documented elsewhere.
Q()
objects
-
class Q
[source]
A Q()
object, like an F
object, encapsulates a SQL expression in a Python object that can be used in database-related operations.
In general, Q() objects
make it possible to define and reuse conditions. This permits the construction of complex database queries using |
(OR
) and &
(AND
) operators; in particular, it is not otherwise possible to use OR
in QuerySets
.
Prefetch()
objects
-
class Prefetch(lookup, queryset=None, to_attr=None)
[source]
The Prefetch()
object can be used to control the operation of prefetch_related()
.
The lookup
argument describes the relations to follow and works the same as the string based lookups passed to prefetch_related()
. For example:
>>> Question.objects.prefetch_related(Prefetch('choice_set')).get().choice_set.all() [<Choice: Not much>, <Choice: The sky>, <Choice: Just hacking again>] # This will only execute two queries regardless of the number of Question # and Choice objects. >>> Question.objects.prefetch_related(Prefetch('choice_set')).all() [<Question: Question object>]
The queryset
argument supplies a base QuerySet
for the given lookup. This is useful to further filter down the prefetch operation, or to call select_related()
from the prefetched relation, hence reducing the number of queries even further:
>>> voted_choices = Choice.objects.filter(votes__gt=0) >>> voted_choices [<Choice: The sky>] >>> prefetch = Prefetch('choice_set', queryset=voted_choices) >>> Question.objects.prefetch_related(prefetch).get().choice_set.all() [<Choice: The sky>]
The to_attr
argument sets the result of the prefetch operation to a custom attribute:
>>> prefetch = Prefetch('choice_set', queryset=voted_choices, to_attr='voted_choices') >>> Question.objects.prefetch_related(prefetch).get().voted_choices [<Choice: The sky>] >>> Question.objects.prefetch_related(prefetch).get().choice_set.all() [<Choice: Not much>, <Choice: The sky>, <Choice: Just hacking again>]
Note
When using to_attr
the prefetched result is stored in a list. This can provide a significant speed improvement over traditional prefetch_related
calls which store the cached result within a QuerySet
instance.
© Django Software Foundation and individual contributors
Licensed under the BSD License.
https://docs.djangoproject.com/en/1.9/ref/models/querysets/