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.
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 unevaluated
QuerySet
usually returns another unevaluated QuerySet
, but Django
will execute the database query if you use the "step" parameter of slice
syntax, and will return a list. Slicing a QuerySet
that has been
evaluated also returns a list.
Also note that even though slicing an unevaluated QuerySet
returns
another unevaluated QuerySet
, 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 call repr()
on it.
This is for convenience in the Python interactive interpreter, so you can
immediately see your results when using the API interactively.
len(). A QuerySet
is evaluated when you call len()
on it.
This, as you might expect, returns the length of the result list.
Note: 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 a
count()
method for precisely this reason.
list(). Force evaluation of a QuerySet
by calling list()
on
it. For example:
entry_list = list(Entry.objects.all())
bool(). Testing a QuerySet
in a boolean context, such as using
bool()
, or
, and
or an if
statement, will cause the query
to be executed. If there is at least one result, the QuerySet
is
True
, otherwise False
. For example:
if Entry.objects.filter(headline="Test"):
print("There is at least one Entry with the headline Test")
Note: 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()
.
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
:
QuerySet
(model=None, query=None, using=None)[源代码]¶Usually when you'll interact with a QuerySet
you'll use it by
chaining filters. To make this work, most
QuerySet
methods return new querysets. These methods are covered in
detail later in this section.
The QuerySet
class has two public attributes you can use for
introspection:
ordered
¶True
if the QuerySet
is ordered — i.e. has an
order_by()
clause or a default ordering on the model.
False
otherwise.
db
¶The database that will be used if this query is executed now.
注解
The query
parameter to QuerySet
exists so that specialized
query subclasses 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.
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
.
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')
You can also order by query expressions by
calling asc()
or desc()
on the expression:
Entry.objects.order_by(Coalesce('summary', 'headline').desc())
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.
注解
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())
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')
警告
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.
If a query doesn't have an ordering specified, results are returned from
the database in an unspecified order. A particular ordering is guaranteed
only when ordering by a set of fields that uniquely identify each object in
the results. For example, if a name
field isn't unique, ordering by it
won't guarantee objects with the same name always appear in the same order.
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()
.
注解
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.
注解
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')
[...]
注解
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, **expressions)¶Returns a QuerySet
that returns dictionaries, rather than model instances,
when used as an iterable.
Each of those dictionaries represents an object, with the keys corresponding to the attribute names of model objects.
This example compares the dictionaries of values()
with the normal model
objects:
# This list contains a Blog object.
>>> Blog.objects.filter(name__startswith='Beatles')
<QuerySet [<Blog: Beatles Blog>]>
# This list contains a dictionary.
>>> Blog.objects.filter(name__startswith='Beatles').values()
<QuerySet [{'id': 1, 'name': 'Beatles Blog', 'tagline': 'All the latest Beatles news.'}]>
The values()
method takes optional positional arguments, *fields
, which
specify field names to which the SELECT
should be limited. If you specify
the fields, each dictionary will contain only the field keys/values for the
fields you specify. If you don't specify the fields, each dictionary will
contain a key and value for every field in the database table.
Example:
>>> Blog.objects.values()
<QuerySet [{'id': 1, 'name': 'Beatles Blog', 'tagline': 'All the latest Beatles news.'}]>
>>> Blog.objects.values('id', 'name')
<QuerySet [{'id': 1, 'name': 'Beatles Blog'}]>
The values()
method also takes optional keyword arguments,
**expressions
, which are passed through to annotate()
:
>>> from django.db.models.functions import Lower
>>> Blog.objects.values(lower_name=Lower('name'))
<QuerySet [{'lower_name': 'beatles blog'}]>
You can use built-in and custom lookups in ordering. For example:
>>> from django.db.models import CharField
>>> from django.db.models.functions import Lower
>>> CharField.register_lookup(Lower)
>>> Blog.objects.values('name__lower')
<QuerySet [{'name__lower': 'beatles blog'}]>
Support for lookups was added.
An aggregate within a values()
clause is applied before other arguments
within the same values()
clause. If you need to group by another value,
add it to an earlier values()
clause instead. For example:
>>> from django.db.models import Count
>>> Blog.objects.values('entry__authors', entries=Count('entry'))
<QuerySet [{'entry__authors': 1, 'entries': 20}, {'entry__authors': 1, 'entries': 13}]>
>>> Blog.objects.values('entry__authors').annotate(entries=Count('entry'))
<QuerySet [{'entry__authors': 1, 'entries': 33}]>
A few subtleties that are worth mentioning:
If you have a field called foo
that is a
ForeignKey
, the default values()
call
will return a dictionary key called foo_id
, since this is the name
of the hidden model attribute that stores the actual value (the foo
attribute refers to the related model). When you are calling
values()
and passing in field names, you can pass in either foo
or foo_id
and you will get back the same thing (the dictionary key
will match the field name you passed in).
For example:
>>> Entry.objects.values()
<QuerySet [{'blog_id': 1, 'headline': 'First Entry', ...}, ...]>
>>> Entry.objects.values('blog')
<QuerySet [{'blog': 1}, ...]>
>>> Entry.objects.values('blog_id')
<QuerySet [{'blog_id': 1}, ...]>
When using values()
together with distinct()
, be aware that
ordering can affect the results. See the note in distinct()
for
details.
If you use a values()
clause after an extra()
call,
any fields defined by a select
argument in the extra()
must
be explicitly included in the values()
call. Any extra()
call
made after a values()
call will have its extra selected fields
ignored.
Calling only()
and defer()
after values()
doesn't make
sense, so doing so will raise a NotImplementedError
.
Combining transforms and aggregates requires the use of two annotate()
calls, either explicitly or as keyword arguments to values()
. As above,
if the transform has been registered on the relevant field type the first
annotate()
can be omitted, thus the following examples are equivalent:
>>> from django.db.models import CharField, Count
>>> from django.db.models.functions import Lower
>>> CharField.register_lookup(Lower)
>>> Blog.objects.values('entry__authors__name__lower').annotate(entries=Count('entry'))
<QuerySet [{'entry__authors__name__lower': 'test author', 'entries': 33}]>
>>> Blog.objects.values(
... entry__authors__name__lower=Lower('entry__authors__name')
... ).annotate(entries=Count('entry'))
<QuerySet [{'entry__authors__name__lower': 'test author', 'entries': 33}]>
>>> Blog.objects.annotate(
... entry__authors__name__lower=Lower('entry__authors__name')
... ).values('entry__authors__name__lower').annotate(entries=Count('entry'))
<QuerySet [{'entry__authors__name__lower': 'test author', 'entries': 33}]>
It is useful when you know you're only going to need values from a small number of the available fields and you won't need the functionality of a model instance object. It's more efficient to select only the fields you need to use.
Finally, note that you can call filter()
, order_by()
, etc. after the
values()
call, that means that these two calls are identical:
Blog.objects.values().order_by('id')
Blog.objects.order_by('id').values()
The people who made Django prefer to put all the SQL-affecting methods first,
followed (optionally) by any output-affecting methods (such as values()
),
but it doesn't really matter. This is your chance to really flaunt your
individualism.
You can also refer to fields on related models with reverse relations through
OneToOneField
, ForeignKey
and ManyToManyField
attributes:
>>> Blog.objects.values('name', 'entry__headline')
<QuerySet [{'name': 'My blog', 'entry__headline': 'An entry'},
{'name': 'My blog', 'entry__headline': 'Another entry'}, ...]>
警告
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, named=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 or expression passed into the values_list()
call — so the
first item is the first field, etc. For example:
>>> Entry.objects.values_list('id', 'headline')
<QuerySet [(1, 'First entry'), ...]>
>>> from django.db.models.functions import Lower
>>> Entry.objects.values_list('id', Lower('headline'))
<QuerySet [(1, 'first entry'), ...]>
If you only pass in a single field, you can also pass in the flat
parameter. If True
, this will mean the returned results are single values,
rather than one-tuples. An example should make the difference clearer:
>>> Entry.objects.values_list('id').order_by('id')
<QuerySet[(1,), (2,), (3,), ...]>
>>> Entry.objects.values_list('id', flat=True).order_by('id')
<QuerySet [1, 2, 3, ...]>
It is an error to pass in flat
when there is more than one field.
You can pass named=True
to get results as a
namedtuple()
:
>>> Entry.objects.values_list('id', 'headline', named=True)
<QuerySet [Row(id=1, headline='First entry'), ...]>
Using a named tuple may make use of the results more readable, at the expense of a small performance penalty for transforming the results into a named tuple.
If you don't pass any values to values_list()
, it will return all the
fields in the model, in the order they were declared.
A common need is to get a specific field value of a certain model instance. To
achieve that, use values_list()
followed by a get()
call:
>>> Entry.objects.values_list('headline', flat=True).get(pk=1)
'First entry'
values()
and values_list()
are both intended as optimizations for a
specific use case: retrieving a subset of data without the overhead of creating
a model instance. This metaphor falls apart when dealing with many-to-many and
other multivalued relations (such as the one-to-many relation of a reverse
foreign key) because the "one row, one object" assumption doesn't hold.
For example, notice the behavior when querying across a
ManyToManyField
:
>>> Author.objects.values_list('name', 'entry__headline')
<QuerySet [('Noam Chomsky', 'Impressions of Gaza'),
('George Orwell', 'Why Socialists Do Not Believe in Fun'),
('George Orwell', 'In Defence of English Cooking'),
('Don Quixote', None)]>
Authors with multiple entries appear multiple times and authors without any
entries have None
for the entry headline.
Similarly, when querying a reverse foreign key, None
appears for entries
not having any author:
>>> Entry.objects.values_list('authors')
<QuerySet [('Noam Chomsky',), ('George Orwell',), (None,)]>
The named
parameter was added.
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"
, "week"
, or "day"
.
Each datetime.date
object in the result list is "truncated" to the
given type
.
"year"
returns a list of all distinct year values for the field."month"
returns a list of all distinct year/month values for the
field."week"
returns a list of all distinct year/week values for the field. All
dates will be a Monday."day"
returns a list of all distinct year/month/day values for the
field.order
, which defaults to 'ASC'
, should be either 'ASC'
or
'DESC'
. This specifies how to order the results.
Examples:
>>> Entry.objects.dates('pub_date', 'year')
[datetime.date(2005, 1, 1)]
>>> Entry.objects.dates('pub_date', 'month')
[datetime.date(2005, 2, 1), datetime.date(2005, 3, 1)]
>>> Entry.objects.dates('pub_date', 'week')
[datetime.date(2005, 2, 14), datetime.date(2005, 3, 14)]
>>> Entry.objects.dates('pub_date', 'day')
[datetime.date(2005, 2, 20), datetime.date(2005, 3, 20)]
>>> Entry.objects.dates('pub_date', 'day', order='DESC')
[datetime.date(2005, 3, 20), datetime.date(2005, 2, 20)]
>>> Entry.objects.filter(headline__contains='Lennon').dates('pub_date', 'day')
[datetime.date(2005, 3, 20)]
"week" support was added.
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"
, "week"
, "day"
,
"hour"
, "minute"
, or "second"
. Each datetime.datetime
object in the result list is "truncated" to the given type
.
order
, which defaults to 'ASC'
, should be either 'ASC'
or
'DESC'
. This specifies how to order the results.
tzinfo
defines the time zone to which datetimes are converted prior to
truncation. Indeed, a given datetime has different representations depending
on the time zone in use. This parameter must be a datetime.tzinfo
object. If it's None
, Django uses the current time zone. It has no effect when USE_TZ
is
False
.
"week" support was added.
注解
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:
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()
<QuerySet []>
>>> 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
.
union()
¶union
(*other_qs, all=False)¶Uses SQL's UNION
operator to combine the results of two or more
QuerySet
s. For example:
>>> qs1.union(qs2, qs3)
The UNION
operator selects only distinct values by default. To allow
duplicate values, use the all=True
argument.
union()
, intersection()
, and difference()
return model instances
of the type of the first QuerySet
even if the arguments are QuerySet
s
of other models. Passing different models works as long as the SELECT
list
is the same in all QuerySet
s (at least the types, the names don't matter
as long as the types in the same order). In such cases, you must use the column
names from the first QuerySet
in QuerySet
methods applied to the
resulting QuerySet
. For example:
>>> qs1 = Author.objects.values_list('name')
>>> qs2 = Entry.objects.values_list('headline')
>>> qs1.union(qs2).order_by('name')
In addition, only LIMIT
, OFFSET
, COUNT(*)
, ORDER BY
, and
specifying columns (i.e. slicing, count()
, order_by()
, and
values()
/values_list()
) are allowed on the resulting
QuerySet
. Further, databases place restrictions on what operations are
allowed in the combined queries. For example, most databases don't allow
LIMIT
or OFFSET
in the combined queries.
intersection()
¶intersection
(*other_qs)¶Uses SQL's INTERSECT
operator to return the shared elements of two or more
QuerySet
s. For example:
>>> qs1.intersection(qs2, qs3)
See union()
for some restrictions.
difference()
¶difference
(*other_qs)¶Uses SQL's EXCEPT
operator to keep only elements present in the
QuerySet
but not in some other QuerySet
s. For example:
>>> qs1.difference(qs2, qs3)
See union()
for some restrictions.
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).
警告
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.
You also must not quote placeholders in the SQL string. This example is
vulnerable to SQL injection because of the quotes around %s
:
"select col from sometable where othercol = '%s'" # unsafe!
You can read more about how Django's SQL injection protection works.
By definition, these extra lookups may not be portable to different database engines (because you're explicitly writing SQL code) and violate the DRY principle, so you should avoid them if possible.
Specify one or more of params
, select
, where
or tables
. None
of the arguments is required, but you should use at least one of them.
select
The select
argument lets you put extra fields in the SELECT
clause. It should be a dictionary mapping attribute names to SQL
clauses to use to calculate that attribute.
Example:
Entry.objects.extra(select={'is_recent': "pub_date > '2006-01-01'"})
As a result, each Entry
object will have an extra attribute,
is_recent
, a boolean representing whether the entry's pub_date
is greater than Jan. 1, 2006.
Django inserts the given SQL snippet directly into the SELECT
statement, so the resulting SQL of the above example would be something
like:
SELECT blog_entry.*, (pub_date > '2006-01-01') AS is_recent
FROM blog_entry;
The next example is more advanced; it does a subquery to give each
resulting Blog
object an entry_count
attribute, an integer count
of associated Entry
objects:
Blog.objects.extra(
select={
'entry_count': 'SELECT COUNT(*) FROM blog_entry WHERE blog_entry.blog_id = blog_blog.id'
},
)
In this particular case, we're exploiting the fact that the query will
already contain the blog_blog
table in its FROM
clause.
The resulting SQL of the above example would be:
SELECT blog_blog.*, (SELECT COUNT(*) FROM blog_entry WHERE blog_entry.blog_id = blog_blog.id) AS entry_count
FROM blog_blog;
Note that the parentheses required by most database engines around
subqueries are not required in Django's select
clauses. Also note
that some database backends, such as some MySQL versions, don't support
subqueries.
In some rare cases, you might wish to pass parameters to the SQL
fragments in extra(select=...)
. For this purpose, use the
select_params
parameter. Since select_params
is a sequence and
the select
attribute is a dictionary, some care is required so that
the parameters are matched up correctly with the extra select pieces.
In this situation, you should use a collections.OrderedDict
for
the select
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
.
where
/ tables
You can define explicit SQL WHERE
clauses — perhaps to perform
non-explicit joins — by using where
. You can manually add tables to
the SQL FROM
clause by using tables
.
where
and tables
both take a list of strings. All where
parameters are "AND"ed to any other search criteria.
Example:
Entry.objects.extra(where=["foo='a' OR bar = 'a'", "baz = 'a'"])
...translates (roughly) into the following SQL:
SELECT * FROM blog_entry WHERE (foo='a' OR bar='a') AND (baz='a')
Be careful when using the tables
parameter if you're specifying
tables that are already used in the query. When you add extra tables
via the tables
parameter, Django assumes you want that table
included an extra time, if it is already included. That creates a
problem, since the table name will then be given an alias. If a table
appears multiple times in an SQL statement, the second and subsequent
occurrences must use aliases so the database can tell them apart. If
you're referring to the extra table you added in the extra where
parameter this is going to cause errors.
Normally you'll only be adding extra tables that don't already appear
in the query. However, if the case outlined above does occur, there are
a few solutions. First, see if you can get by without including the
extra table and use the one already in the query. If that isn't
possible, put your extra()
call at the front of the queryset
construction so that your table is the first use of that table.
Finally, if all else fails, look at the query produced and rewrite your
where
addition to use the alias given to your extra table. The
alias will be the same each time you construct the queryset in the same
way, so you can rely upon the alias name to not change.
order_by
If you need to order the resulting queryset using some of the new
fields or tables you have included via extra()
use the order_by
parameter to extra()
and pass in a sequence of strings. These
strings should either be model fields (as in the normal
order_by()
method on querysets), of the form
table_name.column_name
or an alias for a column that you specified
in the select
parameter to extra()
.
For example:
q = Entry.objects.extra(select={'is_recent': "pub_date > '2006-01-01'"})
q = q.extra(order_by = ['-is_recent'])
This would sort all the items for which is_recent
is true to the
front of the result set (True
sorts before False
in a
descending ordering).
This shows, by the way, that you can make multiple calls to extra()
and it will behave as you expect (adding new constraints each time).
params
The where
parameter described above may use standard Python
database string placeholders — '%s'
to indicate parameters the
database engine should automatically quote. The params
argument is
a list of any extra parameters to be substituted.
Example:
Entry.objects.extra(where=['headline=%s'], params=['Lennon'])
Always use params
instead of embedding values directly into
where
because params
will ensure values are quoted correctly
according to your particular backend. For example, quotes will be
escaped correctly.
Bad:
Entry.objects.extra(where=["headline='Lennon'"])
Good:
Entry.objects.extra(where=['headline=%s'], params=['Lennon'])
警告
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.
注解
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.
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.
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, skip_locked=False, of=())¶Returns a queryset that will lock rows until the end of the transaction,
generating a SELECT ... FOR UPDATE
SQL statement on supported databases.
For example:
from django.db import transaction
entries = Entry.objects.select_for_update().filter(author=request.user)
with transaction.atomic():
for entry in entries:
...
When the queryset is evaluated (for entry in entries
in this case), all
matched entries will be locked until the end of the transaction block, meaning
that other transactions will be prevented from changing or acquiring locks on
them.
Usually, if another transaction has already acquired a lock on one of the
selected rows, the query will block until the lock is released. If this is
not the behavior you want, call select_for_update(nowait=True)
. This will
make the call non-blocking. If a conflicting lock is already acquired by
another transaction, DatabaseError
will be raised when the
queryset is evaluated. You can also ignore locked rows by using
select_for_update(skip_locked=True)
instead. The nowait
and
skip_locked
are mutually exclusive and attempts to call
select_for_update()
with both options enabled will result in a
ValueError
.
By default, select_for_update()
locks all rows that are selected by the
query. For example, rows of related objects specified in select_related()
are locked in addition to rows of the queryset's model. If this isn't desired,
specify the related objects you want to lock in select_for_update(of=(...))
using the same fields syntax as select_related()
. Use the value 'self'
to refer to the queryset's model.
You can't use select_for_update()
on nullable relations:
>>> Person.objects.select_related('hometown').select_for_update()
Traceback (most recent call last):
...
django.db.utils.NotSupportedError: FOR UPDATE cannot be applied to the nullable side of an outer join
To avoid that restriction, you can exclude null objects if you don't care about them:
>>> Person.objects.select_related('hometown').select_for_update().exclude(hometown=None)
<QuerySet [<Person: ...)>, ...]>
Currently, the postgresql
, oracle
, and mysql
database
backends support select_for_update()
. However, MySQL doesn't support the
nowait
, skip_locked
, and of
arguments.
Passing nowait=True
, skip_locked=True
, or of
to
select_for_update()
using database backends that do not support these
options, such as MySQL, raises a NotSupportedError
. This
prevents code from unexpectedly blocking.
Evaluating a queryset with select_for_update()
in autocommit mode on
backends which support SELECT ... FOR UPDATE
is a
TransactionManagementError
error because the
rows are not locked in that case. If allowed, this would facilitate data
corruption and could easily be caused by calling code that expects to be run in
a transaction outside of one.
Using select_for_update()
on backends which do not support
SELECT ... FOR UPDATE
(such as SQLite) will have no effect.
SELECT ... FOR UPDATE
will not be added to the query, and an error isn't
raised if select_for_update()
is used in autocommit mode.
警告
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
.
Certain expressions may not be supported
PostgreSQL doesn't support select_for_update()
with
Window
expressions.
The of
argument was added.
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.
警告
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.
QuerySet
s¶Combined querysets must use the same model.
&
)¶Combines two QuerySet
s using the SQL AND
operator.
The following are equivalent:
Model.objects.filter(x=1) & Model.objects.filter(y=2)
Model.objects.filter(x=1, y=2)
from django.db.models import Q
Model.objects.filter(Q(x=1) & Q(y=2))
SQL equivalent:
SELECT ... WHERE x=1 AND y=2
|
)¶Combines two QuerySet
s using the SQL OR
operator.
The following are equivalent:
Model.objects.filter(x=1) | Model.objects.filter(y=2)
from django.db.models import Q
Model.objects.filter(Q(x=1) | Q(y=2))
SQL equivalent:
SELECT ... WHERE x=1 OR y=2
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.")
If you expect a queryset to return one row, you can use get()
without any
arguments to return the object for that row:
entry = Entry.objects.filter(...).exclude(...).get()
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
.
You can specify more complex conditions for the retrieved object by chaining
get_or_create()
with filter()
and using Q objects
. For example, to retrieve Robert or Bob Marley if either
exists, and create the latter otherwise:
from django.db.models import Q
obj, created = Person.objects.filter(
Q(first_name='Bob') | Q(first_name='Robert'),
).get_or_create(last_name='Marley', defaults={'first_name': 'Bob'})
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({k: v() if callable(v) else v for k, v in defaults.items()})
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. If there are any
callables in defaults
, evaluate them. 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.
警告
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. The values in defaults
can be callables.
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.
Like get_or_create()
and create()
, if you're using manually
specified primary keys and an object needs to be created but the key already
exists in the database, an IntegrityError
is raised.
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='This is a test'),
... Entry(headline='This is only a test'),
... ])
This has a number of caveats though:
The model's save()
method will not be called, and the pre_save
and
post_save
signals will not be sent.
It does not work with child models in a multi-table inheritance scenario.
If the model's primary key is an AutoField
it
does not retrieve and set the primary key attribute, as save()
does,
unless the database backend supports it (currently PostgreSQL).
It does not work with many-to-many relationships.
It casts objs
to a list, which fully evaluates objs
if it's a
generator. The cast allows inspecting all objects so that any objects with a
manually set primary key can be inserted first. If you want to insert objects
in batches without evaluating the entire generator at once, you can use this
technique as long as the objects don't have any manually set primary keys:
from itertools import islice
batch_size = 100
objs = (Entry(headline='Test %s' % i) for i in range(1000))
while True:
batch = list(islice(objs, batch_size))
if not batch:
break
Entry.objects.bulk_create(batch, batch_size)
The batch_size
parameter controls how many objects are created in a 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
.
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).
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=None, field_name='pk')¶Takes a list of field values (id_list
) and the field_name
for those
values, and returns a dictionary mapping each value to an instance of the
object with the given field value. If id_list
isn't provided, all objects
in the queryset are returned. field_name
must be a unique field, and it
defaults to the primary key.
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([])
{}
>>> Blog.objects.in_bulk()
{1: <Blog: Beatles Blog>, 2: <Blog: Cheddar Talk>, 3: <Blog: Django Weblog>}
>>> Blog.objects.in_bulk(['beatles_blog'], field_name='slug')
{'beatles_blog': <Blog: Beatles Blog>}
If you pass in_bulk()
an empty list, you'll get an empty dictionary.
The field_name
parameter was added.
iterator()
¶iterator
(chunk_size=2000)¶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.
Depending on the database backend, query results will either be loaded all at once or streamed from the database using server-side cursors.
Oracle and PostgreSQL use server-side cursors to stream results from the database without loading the entire result set into memory.
The Oracle database driver always uses server-side cursors.
With server-side cursors, the chunk_size
parameter specifies the number of
results to cache at the database driver level. Fetching bigger chunks
diminishes the number of round trips between the database driver and the
database, at the expense of memory.
On PostgreSQL, server-side cursors will only be used when the
DISABLE_SERVER_SIDE_CURSORS
setting is False
. Read Transaction pooling and server-side cursors if
you're using a connection pooler configured in transaction pooling mode. When
server-side cursors are disabled, the behavior is the same as databases that
don't support server-side cursors.
MySQL and SQLite don't support streaming results, hence the Python database
drivers load the entire result set into memory. The result set is then
transformed into Python row objects by the database adapter using the
fetchmany()
method defined in PEP 249.
The chunk_size
parameter controls the size of batches Django retrieves from
the database driver. Larger batches decrease the overhead of communicating with
the database driver at the expense of a slight increase in memory consumption.
The default value of chunk_size
, 2000, comes from a calculation on the
psycopg mailing list:
Assuming rows of 10-20 columns with a mix of textual and numeric data, 2000 is going to fetch less than 100KB of data, which seems a good compromise between the number of rows transferred and the data discarded if the loop is exited early.
The chunk_size
parameter was added.
latest()
¶latest
(*fields)¶Returns the latest object in the table based on the given field(s).
This example returns the latest Entry
in the table, according to the
pub_date
field:
Entry.objects.latest('pub_date')
You can also choose the latest based on several fields. For example, to select
the Entry
with the earliest expire_date
when two entries have the same
pub_date
:
Entry.objects.latest('pub_date', '-expire_date')
The negative sign in '-expire_date'
means to sort expire_date
in
descending order. Since latest()
gets the last result, the Entry
with
the earliest expire_date
is selected.
If your model's Meta specifies
get_latest_by
, you can omit any arguments to
earliest()
or latest()
. The fields specified in
get_latest_by
will be used 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.
Support for several arguments was added.
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')
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. This can affect
aggregation results as described in Interaction with default ordering or order_by().
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
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})
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()
¶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.
explain()
¶explain
(format=None, **options)¶Returns a string of the QuerySet
’s execution plan, which details how the
database would execute the query, including any indexes or joins that would be
used. Knowing these details may help you improve the performance of slow
queries.
For example, when using PostgreSQL:
>>> print(Blog.objects.filter(title='My Blog').explain())
Seq Scan on blog (cost=0.00..35.50 rows=10 width=12)
Filter: (title = 'My Blog'::bpchar)
The output differs significantly between databases.
explain()
is supported by all built-in database backends except Oracle
because an implementation there isn't straightforward.
The format
parameter changes the output format from the databases's default,
usually text-based. PostgreSQL supports 'TEXT'
, 'JSON'
, 'YAML'
, and
'XML'
. MySQL supports 'TEXT'
(also called 'TRADITIONAL'
) and
'JSON'
.
Some databases accept flags that can return more information about the query. Pass these flags as keyword arguments. For example, when using PostgreSQL:
>>> print(Blog.objects.filter(title='My Blog').explain(verbose=True))
Seq Scan on public.blog (cost=0.00..35.50 rows=10 width=12) (actual time=0.004..0.004 rows=10 loops=1)
Output: id, title
Filter: (blog.title = 'My Blog'::bpchar)
Planning time: 0.064 ms
Execution time: 0.058 ms
On some databases, flags may cause the query to be executed which could have
adverse effects on your database. For example, PostgreSQL's ANALYZE
flag
could result in changes to data if there are triggers or if a function is
called, even for a SELECT
query.
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 non-ASCII strings, bear in mind the database note about string comparisons. SQLite does not do case-insensitive matching for non-ASCII 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 non-ASCII strings, bear in mind the database note about string comparisons.
in
¶In a given iterable; often a list, tuple, or queryset. It's not a common use case, but strings (being iterables) are accepted.
Examples:
Entry.objects.filter(id__in=[1, 3, 4])
Entry.objects.filter(headline__in='abc')
SQL equivalents:
SELECT ... WHERE id IN (1, 3, 4);
SELECT ... WHERE headline IN ('a', 'b', 'c');
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.
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='Lennon')
SQL equivalent:
SELECT ... WHERE headline LIKE 'Lennon%';
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='Lennon')
SQL equivalent:
SELECT ... WHERE headline ILIKE 'Lennon%';
SQLite users
When using the SQLite backend and non-ASCII strings, bear in mind the database note about string comparisons.
endswith
¶Case-sensitive ends-with.
Example:
Entry.objects.filter(headline__endswith='Lennon')
SQL equivalent:
SELECT ... WHERE headline LIKE '%Lennon';
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='Lennon')
SQL equivalent:
SELECT ... WHERE headline ILIKE '%Lennon'
SQLite users
When using the SQLite backend and 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.
警告
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.
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.
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.
week
¶For date and datetime fields, return the week number (1-52 or 53) according to ISO-8601, i.e., weeks start on a Monday and the first week contains the year's first Thursday.
Example:
Entry.objects.filter(pub_date__week=52)
Entry.objects.filter(pub_date__week__gte=32, pub_date__week__lte=38)
(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.
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.
quarter
¶For date and datetime fields, a 'quarter of the year' match. Allows chaining additional field lookups. Takes an integer value between 1 and 4 representing the quarter of the year.
Example to retrieve entries in the second quarter (April 1 to June 30):
Entry.objects.filter(pub_date__quarter=2)
(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
, datetime fields are converted to the
current time zone before filtering. This requires time zone definitions
in the database.
time
¶For datetime fields, casts the value as time. Allows chaining additional field
lookups. Takes a datetime.time
value.
Example:
Entry.objects.filter(pub_date__time=datetime.time(14, 30))
Entry.objects.filter(pub_date__time__range=(datetime.time(8), datetime.time(17)))
(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.
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.
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.
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.
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;
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
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.
警告
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:
expressions
¶Strings that reference fields on the model, or query expressions.
output_field
¶An optional argument that represents the model field of the return value
注解
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.
filter
¶An optional Q object
that's used to filter the
rows that are aggregated.
See Conditional aggregation and Filtering on annotations for example usage.
**extra
¶Keyword arguments that can provide extra context for the SQL generated by the aggregate.
Avg
¶Count
¶Count
(expression, distinct=False, filter=None, **extra)[源代码]¶Returns the number of objects that are related through the provided expression.
<field>__count
int
Has one optional argument:
distinct
¶If distinct=True
, the count will only include unique instances.
This is the SQL equivalent of COUNT(DISTINCT <field>)
. The default
value is False
.
Max
¶Min
¶StdDev
¶StdDev
(expression, sample=False, filter=None, **extra)[源代码]¶Returns the standard deviation of the data in the provided expression.
<field>__stddev
float
Has one optional argument:
sample
¶By default, StdDev
returns the population standard deviation. However,
if sample=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.
Sum
¶Variance
¶Variance
(expression, sample=False, filter=None, **extra)[源代码]¶Returns the variance of the data in the provided expression.
<field>__variance
float
Has one optional argument:
sample
¶By default, Variance
returns the population variance. However,
if sample=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.
1月 11, 2019