Query expressions describe a value or a computation that can be used as part of an update, create, filter, order by, annotation, or aggregate. When an expression outputs a boolean value, it may be used directly in filters. There are a number of built-in expressions (documented below) that can be used to help you write queries. Expressions can be combined, or in some cases nested, to form more complex computations.
Django supports negation, addition, subtraction, multiplication, division, modulo arithmetic, and the power operator on query expressions, using Python constants, variables, and even other expressions.
from django.db.models import Count, F, Value
from django.db.models.functions import Length, Upper
from django.db.models.lookups import GreaterThan
# Find companies that have more employees than chairs.
Company.objects.filter(num_employees__gt=F('num_chairs'))
# Find companies that have at least twice as many employees
# as chairs. Both the querysets below are equivalent.
Company.objects.filter(num_employees__gt=F('num_chairs') * 2)
Company.objects.filter(
num_employees__gt=F('num_chairs') + F('num_chairs'))
# How many chairs are needed for each company to seat all employees?
>>> company = Company.objects.filter(
... num_employees__gt=F('num_chairs')).annotate(
... chairs_needed=F('num_employees') - F('num_chairs')).first()
>>> company.num_employees
120
>>> company.num_chairs
50
>>> company.chairs_needed
70
# Create a new company using expressions.
>>> company = Company.objects.create(name='Google', ticker=Upper(Value('goog')))
# Be sure to refresh it if you need to access the field.
>>> company.refresh_from_db()
>>> company.ticker
'GOOG'
# Annotate models with an aggregated value. Both forms
# below are equivalent.
Company.objects.annotate(num_products=Count('products'))
Company.objects.annotate(num_products=Count(F('products')))
# Aggregates can contain complex computations also
Company.objects.annotate(num_offerings=Count(F('products') + F('services')))
# Expressions can also be used in order_by(), either directly
Company.objects.order_by(Length('name').asc())
Company.objects.order_by(Length('name').desc())
# or using the double underscore lookup syntax.
from django.db.models import CharField
from django.db.models.functions import Length
CharField.register_lookup(Length)
Company.objects.order_by('name__length')
# Boolean expression can be used directly in filters.
from django.db.models import Exists
Company.objects.filter(
Exists(Employee.objects.filter(company=OuterRef('pk'), salary__gt=10))
)
# Lookup expressions can also be used directly in filters
Company.objects.filter(GreaterThan(F('num_employees'), F('num_chairs')))
# or annotations.
Company.objects.annotate(
need_chairs=GreaterThan(F('num_employees'), F('num_chairs')),
)
注釈
These expressions are defined in django.db.models.expressions and
django.db.models.aggregates, but for convenience they're available and
usually imported from django.db.models.
F() 式¶F¶An F() object represents the value of a model field, transformed value of a
model field, or annotated column. It makes it possible to refer to model field
values and perform database operations using them without actually having to
pull them out of the database into Python memory.
Instead, Django uses the F() object to generate an SQL expression that
describes the required operation at the database level.
Let's try this with an example. Normally, one might do something like this:
# Tintin filed a news story!
reporter = Reporters.objects.get(name='Tintin')
reporter.stories_filed += 1
reporter.save()
Here, we have pulled the value of reporter.stories_filed from the database
into memory and manipulated it using familiar Python operators, and then saved
the object back to the database. But instead we could also have done:
from django.db.models import F
reporter = Reporters.objects.get(name='Tintin')
reporter.stories_filed = F('stories_filed') + 1
reporter.save()
Although reporter.stories_filed = F('stories_filed') + 1 looks like a
normal Python assignment of value to an instance attribute, in fact it's an SQL
construct describing an operation on the database.
When Django encounters an instance of F(), it overrides the standard Python
operators to create an encapsulated SQL expression; in this case, one which
instructs the database to increment the database field represented by
reporter.stories_filed.
Whatever value is or was on reporter.stories_filed, Python never gets to
know about it - it is dealt with entirely by the database. All Python does,
through Django's F() class, is create the SQL syntax to refer to the field
and describe the operation.
To access the new value saved this way, the object must be reloaded:
reporter = Reporters.objects.get(pk=reporter.pk)
# Or, more succinctly:
reporter.refresh_from_db()
As well as being used in operations on single instances as above, F() can
be used on QuerySets of object instances, with update(). This reduces
the two queries we were using above - the get() and the
save() - to just one:
reporter = Reporters.objects.filter(name='Tintin')
reporter.update(stories_filed=F('stories_filed') + 1)
We can also use update() to increment
the field value on multiple objects - which could be very much faster than
pulling them all into Python from the database, looping over them, incrementing
the field value of each one, and saving each one back to the database:
Reporter.objects.all().update(stories_filed=F('stories_filed') + 1)
F() therefore can offer performance advantages by:
Support for transforms of the field was added.
F()¶Another useful benefit of F() is that having the database - rather than
Python - update a field's value avoids a race condition.
If two Python threads execute the code in the first example above, one thread could retrieve, increment, and save a field's value after the other has retrieved it from the database. The value that the second thread saves will be based on the original value; the work of the first thread will be lost.
If the database is responsible for updating the field, the process is more
robust: it will only ever update the field based on the value of the field in
the database when the save() or update() is executed, rather
than based on its value when the instance was retrieved.
F() assignments persist after Model.save()¶F() objects assigned to model fields persist after saving the model
instance and will be applied on each save(). For example:
reporter = Reporters.objects.get(name='Tintin')
reporter.stories_filed = F('stories_filed') + 1
reporter.save()
reporter.name = 'Tintin Jr.'
reporter.save()
stories_filed will be updated twice in this case. If it's initially 1,
the final value will be 3. This persistence can be avoided by reloading the
model object after saving it, for example, by using
refresh_from_db().
F() in filters¶F() is also very useful in QuerySet filters, where they make it
possible to filter a set of objects against criteria based on their field
values, rather than on Python values.
This is documented in using F() expressions in queries.
F() を annotation と一緒に使う¶F() can be used to create dynamic fields on your models by combining
different fields with arithmetic:
company = Company.objects.annotate(
chairs_needed=F('num_employees') - F('num_chairs'))
If the fields that you're combining are of different types you'll need
to tell Django what kind of field will be returned. Since F() does not
directly support output_field you will need to wrap the expression with
ExpressionWrapper:
from django.db.models import DateTimeField, ExpressionWrapper, F
Ticket.objects.annotate(
expires=ExpressionWrapper(
F('active_at') + F('duration'), output_field=DateTimeField()))
When referencing relational fields such as ForeignKey, F() returns the
primary key value rather than a model instance:
>> car = Company.objects.annotate(built_by=F('manufacturer'))[0]
>> car.manufacturer
<Manufacturer: Toyota>
>> car.built_by
3
F() to sort null values¶Use F() and the nulls_first or nulls_last keyword argument to
Expression.asc() or desc() to control the ordering of
a field's null values. By default, the ordering depends on your database.
For example, to sort companies that haven't been contacted (last_contacted
is null) after companies that have been contacted:
from django.db.models import F
Company.objects.order_by(F('last_contacted').desc(nulls_last=True))
Func() 式¶Func() expressions are the base type of all expressions that involve
database functions like COALESCE and LOWER, or aggregates like SUM.
They can be used directly:
from django.db.models import F, Func
queryset.annotate(field_lower=Func(F('field'), function='LOWER'))
or they can be used to build a library of database functions:
class Lower(Func):
function = 'LOWER'
queryset.annotate(field_lower=Lower('field'))
But both cases will result in a queryset where each model is annotated with an
extra attribute field_lower produced, roughly, from the following SQL:
SELECT
...
LOWER("db_table"."field") as "field_lower"
See Database Functions for a list of built-in database functions.
The Func API is as follows:
Func(*expressions, **extra)¶function¶A class attribute describing the function that will be generated.
Specifically, the function will be interpolated as the function
placeholder within template. Defaults to None.
template¶A class attribute, as a format string, that describes the SQL that is
generated for this function. Defaults to
'%(function)s(%(expressions)s)'.
If you're constructing SQL like strftime('%W', 'date') and need a
literal % character in the query, quadruple it (%%%%) in the
template attribute because the string is interpolated twice: once
during the template interpolation in as_sql() and once in the SQL
interpolation with the query parameters in the database cursor.
arg_joiner¶A class attribute that denotes the character used to join the list of
expressions together. Defaults to ', '.
arity¶A class attribute that denotes the number of arguments the function
accepts. If this attribute is set and the function is called with a
different number of expressions, TypeError will be raised. Defaults
to None.
as_sql(compiler, connection, function=None, template=None, arg_joiner=None, **extra_context)¶Generates the SQL fragment for the database function. Returns a tuple
(sql, params), where sql is the SQL string, and params is
the list or tuple of query parameters.
The as_vendor() methods should use the function, template,
arg_joiner, and any other **extra_context parameters to
customize the SQL as needed. For example:
class ConcatPair(Func):
...
function = 'CONCAT'
...
def as_mysql(self, compiler, connection, **extra_context):
return super().as_sql(
compiler, connection,
function='CONCAT_WS',
template="%(function)s('', %(expressions)s)",
**extra_context
)
To avoid an SQL injection vulnerability, extra_context must
not contain untrusted user input
as these values are interpolated into the SQL string rather than passed
as query parameters, where the database driver would escape them.
The *expressions argument is a list of positional expressions that the
function will be applied to. The expressions will be converted to strings,
joined together with arg_joiner, and then interpolated into the template
as the expressions placeholder.
Positional arguments can be expressions or Python values. Strings are
assumed to be column references and will be wrapped in F() expressions
while other values will be wrapped in Value() expressions.
The **extra kwargs are key=value pairs that can be interpolated
into the template attribute. To avoid an SQL injection vulnerability,
extra must not contain untrusted user input as these values are interpolated
into the SQL string rather than passed as query parameters, where the database
driver would escape them.
The function, template, and arg_joiner keywords can be used to
replace the attributes of the same name without having to define your own
class. output_field can be used to define the expected return type.
Aggregate() 式¶aggregate 式は、GROUP BY 句が必要になるようなクエリーを伝える Func() 式 の特殊な場合です。Sum() や Count() など、すべての aggregate 関数 は、Aggregate() を継承しています。
Since Aggregates are expressions and wrap expressions, you can represent
some complex computations:
from django.db.models import Count
Company.objects.annotate(
managers_required=(Count('num_employees') / 4) + Count('num_managers'))
The Aggregate API is as follows:
Aggregate(*expressions, output_field=None, distinct=False, filter=None, default=None, **extra)¶template¶A class attribute, as a format string, that describes the SQL that is
generated for this aggregate. Defaults to
'%(function)s(%(distinct)s%(expressions)s)'.
function¶A class attribute describing the aggregate function that will be
generated. Specifically, the function will be interpolated as the
function placeholder within template. Defaults to None.
window_compatible¶Defaults to True since most aggregate functions can be used as the
source expression in Window.
allow_distinct¶A class attribute determining whether or not this aggregate function
allows passing a distinct keyword argument. If set to False
(default), TypeError is raised if distinct=True is passed.
empty_result_set_value¶Defaults to None since most aggregate functions result in NULL
when applied to an empty result set.
The expressions positional arguments can include expressions, transforms of
the model field, or the names of model fields. They will be converted to a
string and used as the expressions placeholder within the template.
The output_field argument requires a model field instance, like
IntegerField() or BooleanField(), into which Django will load the value
after it's retrieved from the database. Usually no arguments are needed when
instantiating the model field as any arguments relating to data validation
(max_length, max_digits, etc.) will not be enforced on the expression's
output value.
Note that output_field is only required when Django is unable to determine
what field type the result should be. Complex expressions that mix field types
should define the desired output_field. For example, adding an
IntegerField() and a FloatField() together should probably have
output_field=FloatField() defined.
The distinct argument determines whether or not the aggregate function
should be invoked for each distinct value of expressions (or set of
values, for multiple expressions). The argument is only supported on
aggregates that have allow_distinct set to True.
The filter argument takes a Q object that's
used to filter the rows that are aggregated. See Conditional aggregation
and Filtering on annotations for example usage.
The default argument takes a value that will be passed along with the
aggregate to Coalesce. This is useful for
specifying a value to be returned other than None when the queryset (or
grouping) contains no entries.
The **extra kwargs are key=value pairs that can be interpolated
into the template attribute.
Support for transforms of the field was added.
The default argument was added.
You can create your own aggregate functions, too. At a minimum, you need to
define function, but you can also completely customize the SQL that is
generated. Here's a brief example:
from django.db.models import Aggregate
class Sum(Aggregate):
# Supports SUM(ALL field).
function = 'SUM'
template = '%(function)s(%(all_values)s%(expressions)s)'
allow_distinct = False
def __init__(self, expression, all_values=False, **extra):
super().__init__(
expression,
all_values='ALL ' if all_values else '',
**extra
)
Value() 式¶Value(value, output_field=None)¶A Value() object represents the smallest possible component of an
expression: a simple value. When you need to represent the value of an integer,
boolean, or string within an expression, you can wrap that value within a
Value().
You will rarely need to use Value() directly. When you write the expression
F('field') + 1, Django implicitly wraps the 1 in a Value(),
allowing simple values to be used in more complex expressions. You will need to
use Value() when you want to pass a string to an expression. Most
expressions interpret a string argument as the name of a field, like
Lower('name').
The value argument describes the value to be included in the expression,
such as 1, True, or None. Django knows how to convert these Python
values into their corresponding database type.
The output_field argument should be a model field instance, like
IntegerField() or BooleanField(), into which Django will load the value
after it's retrieved from the database. Usually no arguments are needed when
instantiating the model field as any arguments relating to data validation
(max_length, max_digits, etc.) will not be enforced on the expression's
output value. If no output_field is specified it will be tentatively
inferred from the type of the provided value, if possible. For
example, passing an instance of datetime.datetime as value
would default output_field to DateTimeField.
Support for inferring a default output_field from the type of value
was added.
ExpressionWrapper() 式¶ExpressionWrapper(expression, output_field)¶ExpressionWrapper surrounds another expression and provides access to
properties, such as output_field, that may not be available on other
expressions. ExpressionWrapper is necessary when using arithmetic on
F() expressions with different types as described in
F() を annotation と一緒に使う.
条件式を用いると、if ... elif ... else ロジックをクエリーの中で使用できるようになります。Django はネイティブで SQL CASE 式をサポートしています。詳細については Conditional Expressions を読んでください。
Subquery() 式¶Subquery(queryset, output_field=None)¶You can add an explicit subquery to a QuerySet using the Subquery
expression.
For example, to annotate each post with the email address of the author of the newest comment on that post:
>>> from django.db.models import OuterRef, Subquery
>>> newest = Comment.objects.filter(post=OuterRef('pk')).order_by('-created_at')
>>> Post.objects.annotate(newest_commenter_email=Subquery(newest.values('email')[:1]))
On PostgreSQL, the SQL looks like:
SELECT "post"."id", (
SELECT U0."email"
FROM "comment" U0
WHERE U0."post_id" = ("post"."id")
ORDER BY U0."created_at" DESC LIMIT 1
) AS "newest_commenter_email" FROM "post"
注釈
The examples in this section are designed to show how to force Django to execute a subquery. In some cases it may be possible to write an equivalent queryset that performs the same task more clearly or efficiently.
OuterRef(field)¶Use OuterRef when a queryset in a Subquery needs to refer to a field
from the outer query or its transform. It acts like an F expression
except that the check to see if it refers to a valid field isn't made until the
outer queryset is resolved.
Instances of OuterRef may be used in conjunction with nested instances
of Subquery to refer to a containing queryset that isn't the immediate
parent. For example, this queryset would need to be within a nested pair of
Subquery instances to resolve correctly:
>>> Book.objects.filter(author=OuterRef(OuterRef('pk')))
Support for transforms of the field was added.
There are times when a single column must be returned from a Subquery, for
instance, to use a Subquery as the target of an __in lookup. To return
all comments for posts published within the last day:
>>> from datetime import timedelta
>>> from django.utils import timezone
>>> one_day_ago = timezone.now() - timedelta(days=1)
>>> posts = Post.objects.filter(published_at__gte=one_day_ago)
>>> Comment.objects.filter(post__in=Subquery(posts.values('pk')))
In this case, the subquery must use values()
to return only a single column: the primary key of the post.
To prevent a subquery from returning multiple rows, a slice ([:1]) of the
queryset is used:
>>> subquery = Subquery(newest.values('email')[:1])
>>> Post.objects.annotate(newest_commenter_email=subquery)
In this case, the subquery must only return a single column and a single row: the email address of the most recently created comment.
(Using get() instead of a slice would fail because the
OuterRef cannot be resolved until the queryset is used within a
Subquery.)
Exists() subqueries¶Exists(queryset)¶Exists is a Subquery subclass that uses an SQL EXISTS statement. In
many cases it will perform better than a subquery since the database is able to
stop evaluation of the subquery when a first matching row is found.
For example, to annotate each post with whether or not it has a comment from within the last day:
>>> from django.db.models import Exists, OuterRef
>>> from datetime import timedelta
>>> from django.utils import timezone
>>> one_day_ago = timezone.now() - timedelta(days=1)
>>> recent_comments = Comment.objects.filter(
... post=OuterRef('pk'),
... created_at__gte=one_day_ago,
... )
>>> Post.objects.annotate(recent_comment=Exists(recent_comments))
On PostgreSQL, the SQL looks like:
SELECT "post"."id", "post"."published_at", EXISTS(
SELECT (1) as "a"
FROM "comment" U0
WHERE (
U0."created_at" >= YYYY-MM-DD HH:MM:SS AND
U0."post_id" = "post"."id"
)
LIMIT 1
) AS "recent_comment" FROM "post"
It's unnecessary to force Exists to refer to a single column, since the
columns are discarded and a boolean result is returned. Similarly, since
ordering is unimportant within an SQL EXISTS subquery and would only
degrade performance, it's automatically removed.
You can query using NOT EXISTS with ~Exists().
Subquery() or Exists() expressions¶Subquery() that returns a boolean value and Exists() may be used as a
condition in When expressions, or to
directly filter a queryset:
>>> recent_comments = Comment.objects.filter(...) # From above
>>> Post.objects.filter(Exists(recent_comments))
This will ensure that the subquery will not be added to the SELECT columns,
which may result in a better performance.
Subquery expression¶Aggregates may be used within a Subquery, but they require a specific
combination of filter(), values(), and
annotate() to get the subquery grouping correct.
Assuming both models have a length field, to find posts where the post
length is greater than the total length of all combined comments:
>>> from django.db.models import OuterRef, Subquery, Sum
>>> comments = Comment.objects.filter(post=OuterRef('pk')).order_by().values('post')
>>> total_comments = comments.annotate(total=Sum('length')).values('total')
>>> Post.objects.filter(length__gt=Subquery(total_comments))
The initial filter(...) limits the subquery to the relevant parameters.
order_by() removes the default ordering
(if any) on the Comment model. values('post') aggregates comments by
Post. Finally, annotate(...) performs the aggregation. The order in
which these queryset methods are applied is important. In this case, since the
subquery must be limited to a single column, values('total') is required.
This is the only way to perform an aggregation within a Subquery, as
using aggregate() attempts to evaluate the queryset (and if
there is an OuterRef, this will not be possible to resolve).
RawSQL(sql, params, output_field=None)¶Sometimes database expressions can't easily express a complex WHERE clause.
In these edge cases, use the RawSQL expression. For example:
>>> from django.db.models.expressions import RawSQL
>>> queryset.annotate(val=RawSQL("select col from sometable where othercol = %s", (param,)))
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.
RawSQL expressions can also be used as the target of __in filters:
>>> queryset.filter(id__in=RawSQL("select id from sometable where col = %s", (param,)))
警告
To protect against SQL injection attacks, you must escape any
parameters that the user can control by using params. params is a
required argument to force you to acknowledge that you're not interpolating
your SQL with user-provided data.
You also must not quote placeholders in the SQL string. This example is
vulnerable to SQL injection because of the quotes around %s:
RawSQL("select col from sometable where othercol = '%s'") # unsafe!
You can read more about how Django's SQL injection protection works.
Window functions provide a way to apply functions on partitions. Unlike a normal aggregation function which computes a final result for each set defined by the group by, window functions operate on frames and partitions, and compute the result for each row.
You can specify multiple windows in the same query which in Django ORM would be equivalent to including multiple expressions in a QuerySet.annotate() call. The ORM doesn't make use of named windows, instead they are part of the selected columns.
Window(expression, partition_by=None, order_by=None, frame=None, output_field=None)¶filterable¶Defaults to False. The SQL standard disallows referencing window
functions in the WHERE clause and Django raises an exception when
constructing a QuerySet that would do that.
template¶Defaults to %(expression)s OVER (%(window)s)'. If only the
expression argument is provided, the window clause will be blank.
The Window class is the main expression for an OVER clause.
The expression argument is either a window function, an aggregate function, or
an expression that's compatible in a window clause.
The partition_by argument accepts an expression or a sequence of
expressions (column names should be wrapped in an F-object) that control
the partitioning of the rows. Partitioning narrows which rows are used to
compute the result set.
The output_field is specified either as an argument or by the expression.
The order_by argument accepts an expression or a sequence of expressions on
which you can call asc() and
desc(). The ordering controls the order in
which the expression is applied. For example, if you sum over the rows in a
partition, the first result is the value of the first row, the second is the
sum of first and second row.
The frame parameter specifies which other rows that should be used in the
computation. See Frames for details.
For example, to annotate each movie with the average rating for the movies by the same studio in the same genre and release year:
>>> from django.db.models import Avg, F, Window
>>> from django.db.models.functions import ExtractYear
>>> Movie.objects.annotate(
>>> avg_rating=Window(
>>> expression=Avg('rating'),
>>> partition_by=[F('studio'), F('genre')],
>>> order_by=ExtractYear('released').asc(),
>>> ),
>>> )
This allows you to check if a movie is rated better or worse than its peers.
You may want to apply multiple expressions over the same window, i.e., the same partition and frame. For example, you could modify the previous example to also include the best and worst rating in each movie's group (same studio, genre, and release year) by using three window functions in the same query. The partition and ordering from the previous example is extracted into a dictionary to reduce repetition:
>>> from django.db.models import Avg, F, Max, Min, Window
>>> from django.db.models.functions import ExtractYear
>>> window = {
>>> 'partition_by': [F('studio'), F('genre')],
>>> 'order_by': ExtractYear('released').asc(),
>>> }
>>> Movie.objects.annotate(
>>> avg_rating=Window(
>>> expression=Avg('rating'), **window,
>>> ),
>>> best=Window(
>>> expression=Max('rating'), **window,
>>> ),
>>> worst=Window(
>>> expression=Min('rating'), **window,
>>> ),
>>> )
Among Django's built-in database backends, MySQL 8.0.2+, PostgreSQL, and Oracle
support window expressions. Support for different window expression features
varies among the different databases. For example, the options in
asc() and
desc() may not be supported. Consult the
documentation for your database as needed.
For a window frame, you can choose either a range-based sequence of rows or an ordinary sequence of rows.
ValueRange(start=None, end=None)¶frame_type¶This attribute is set to 'RANGE'.
PostgreSQL has limited support for ValueRange and only supports use of
the standard start and end points, such as CURRENT ROW and UNBOUNDED
FOLLOWING.
Both classes return SQL with the template:
%(frame_type)s BETWEEN %(start)s AND %(end)s
Frames narrow the rows that are used for computing the result. They shift from some start point to some specified end point. Frames can be used with and without partitions, but it's often a good idea to specify an ordering of the window to ensure a deterministic result. In a frame, a peer in a frame is a row with an equivalent value, or all rows if an ordering clause isn't present.
The default starting point for a frame is UNBOUNDED PRECEDING which is the
first row of the partition. The end point is always explicitly included in the
SQL generated by the ORM and is by default UNBOUNDED FOLLOWING. The default
frame includes all rows from the partition to the last row in the set.
The accepted values for the start and end arguments are None, an
integer, or zero. A negative integer for start results in N preceding,
while None yields UNBOUNDED PRECEDING. For both start and end,
zero will return CURRENT ROW. Positive integers are accepted for end.
There's a difference in what CURRENT ROW includes. When specified in
ROWS mode, the frame starts or ends with the current row. When specified in
RANGE mode, the frame starts or ends at the first or last peer according to
the ordering clause. Thus, RANGE CURRENT ROW evaluates the expression for
rows which have the same value specified by the ordering. Because the template
includes both the start and end points, this may be expressed with:
ValueRange(start=0, end=0)
If a movie's "peers" are described as movies released by the same studio in the
same genre in the same year, this RowRange example annotates each movie
with the average rating of a movie's two prior and two following peers:
>>> from django.db.models import Avg, F, RowRange, Window
>>> from django.db.models.functions import ExtractYear
>>> Movie.objects.annotate(
>>> avg_rating=Window(
>>> expression=Avg('rating'),
>>> partition_by=[F('studio'), F('genre')],
>>> order_by=ExtractYear('released').asc(),
>>> frame=RowRange(start=-2, end=2),
>>> ),
>>> )
If the database supports it, you can specify the start and end points based on
values of an expression in the partition. If the released field of the
Movie model stores the release month of each movies, this ValueRange
example annotates each movie with the average rating of a movie's peers
released between twelve months before and twelve months after the each movie.
>>> from django.db.models import Avg, F, ValueRange, Window
>>> Movie.objects.annotate(
>>> avg_rating=Window(
>>> expression=Avg('rating'),
>>> partition_by=[F('studio'), F('genre')],
>>> order_by=F('released').asc(),
>>> frame=ValueRange(start=-12, end=12),
>>> ),
>>> )
Below you'll find technical implementation details that may be useful to library authors. The technical API and examples below will help with creating generic query expressions that can extend the built-in functionality that Django provides.
Query expressions implement the query expression API,
but also expose a number of extra methods and attributes listed below. All
query expressions must inherit from Expression() or a relevant
subclass.
When a query expression wraps another expression, it is responsible for calling the appropriate methods on the wrapped expression.
Expression¶contains_aggregate¶Tells Django that this expression contains an aggregate and that a
GROUP BY clause needs to be added to the query.
contains_over_clause¶Tells Django that this expression contains a
Window expression. It's used,
for example, to disallow window function expressions in queries that
modify data.
filterable¶Tells Django that this expression can be referenced in
QuerySet.filter(). Defaults to True.
window_compatible¶Tells Django that this expression can be used as the source expression
in Window. Defaults to
False.
empty_result_set_value¶Tells Django which value should be returned when the expression is used
to apply a function over an empty result set. Defaults to
NotImplemented which forces the expression to be computed on
the database.
resolve_expression(query=None, allow_joins=True, reuse=None, summarize=False, for_save=False)¶Provides the chance to do any pre-processing or validation of
the expression before it's added to the query. resolve_expression()
must also be called on any nested expressions. A copy() of self
should be returned with any necessary transformations.
query is the backend query implementation.
allow_joins is a boolean that allows or denies the use of
joins in the query.
reuse is a set of reusable joins for multi-join scenarios.
summarize is a boolean that, when True, signals that the
query being computed is a terminal aggregate query.
for_save is a boolean that, when True, signals that the query
being executed is performing a create or update.
get_source_expressions()¶Returns an ordered list of inner expressions. For example:
>>> Sum(F('foo')).get_source_expressions()
[F('foo')]
set_source_expressions(expressions)¶Takes a list of expressions and stores them such that
get_source_expressions() can return them.
relabeled_clone(change_map)¶Returns a clone (copy) of self, with any column aliases relabeled.
Column aliases are renamed when subqueries are created.
relabeled_clone() should also be called on any nested expressions
and assigned to the clone.
change_map is a dictionary mapping old aliases to new aliases.
実装例:
def relabeled_clone(self, change_map):
clone = copy.copy(self)
clone.expression = self.expression.relabeled_clone(change_map)
return clone
convert_value(value, expression, connection)¶A hook allowing the expression to coerce value into a more
appropriate type.
expression is the same as self.
get_group_by_cols(alias=None)¶Responsible for returning the list of columns references by
this expression. get_group_by_cols() should be called on any
nested expressions. F() objects, in particular, hold a reference
to a column. The alias parameter will be None unless the
expression has been annotated and is used for grouping.
asc(nulls_first=False, nulls_last=False)¶Returns the expression ready to be sorted in ascending order.
nulls_first and nulls_last define how null values are sorted.
See Using F() to sort null values for example usage.
desc(nulls_first=False, nulls_last=False)¶Returns the expression ready to be sorted in descending order.
nulls_first and nulls_last define how null values are sorted.
See Using F() to sort null values for example usage.
reverse_ordering()¶Returns self with any modifications required to reverse the sort
order within an order_by call. As an example, an expression
implementing NULLS LAST would change its value to be
NULLS FIRST. Modifications are only required for expressions that
implement sort order like OrderBy. This method is called when
reverse() is called on a
queryset.
You can write your own query expression classes that use, and can integrate
with, other query expressions. Let's step through an example by writing an
implementation of the COALESCE SQL function, without using the built-in
Func() expressions.
The COALESCE SQL function is defined as taking a list of columns or
values. It will return the first column or value that isn't NULL.
We'll start by defining the template to be used for SQL generation and
an __init__() method to set some attributes:
import copy
from django.db.models import Expression
class Coalesce(Expression):
template = 'COALESCE( %(expressions)s )'
def __init__(self, expressions, output_field):
super().__init__(output_field=output_field)
if len(expressions) < 2:
raise ValueError('expressions must have at least 2 elements')
for expression in expressions:
if not hasattr(expression, 'resolve_expression'):
raise TypeError('%r is not an Expression' % expression)
self.expressions = expressions
We do some basic validation on the parameters, including requiring at least
2 columns or values, and ensuring they are expressions. We are requiring
output_field here so that Django knows what kind of model field to assign
the eventual result to.
Now we implement the pre-processing and validation. Since we do not have any of our own validation at this point, we delegate to the nested expressions:
def resolve_expression(self, query=None, allow_joins=True, reuse=None, summarize=False, for_save=False):
c = self.copy()
c.is_summary = summarize
for pos, expression in enumerate(self.expressions):
c.expressions[pos] = expression.resolve_expression(query, allow_joins, reuse, summarize, for_save)
return c
Next, we write the method responsible for generating the SQL:
def as_sql(self, compiler, connection, template=None):
sql_expressions, sql_params = [], []
for expression in self.expressions:
sql, params = compiler.compile(expression)
sql_expressions.append(sql)
sql_params.extend(params)
template = template or self.template
data = {'expressions': ','.join(sql_expressions)}
return template % data, sql_params
def as_oracle(self, compiler, connection):
"""
Example of vendor specific handling (Oracle in this case).
Let's make the function name lowercase.
"""
return self.as_sql(compiler, connection, template='coalesce( %(expressions)s )')
as_sql() methods can support custom keyword arguments, allowing
as_vendorname() methods to override data used to generate the SQL string.
Using as_sql() keyword arguments for customization is preferable to
mutating self within as_vendorname() methods as the latter can lead to
errors when running on different database backends. If your class relies on
class attributes to define data, consider allowing overrides in your
as_sql() method.
We generate the SQL for each of the expressions by using the
compiler.compile() method, and join the result together with commas.
Then the template is filled out with our data and the SQL and parameters
are returned.
We've also defined a custom implementation that is specific to the Oracle
backend. The as_oracle() function will be called instead of as_sql()
if the Oracle backend is in use.
Finally, we implement the rest of the methods that allow our query expression to play nice with other query expressions:
def get_source_expressions(self):
return self.expressions
def set_source_expressions(self, expressions):
self.expressions = expressions
Let's see how it works:
>>> from django.db.models import F, Value, CharField
>>> qs = Company.objects.annotate(
... tagline=Coalesce([
... F('motto'),
... F('ticker_name'),
... F('description'),
... Value('No Tagline')
... ], output_field=CharField()))
>>> for c in qs:
... print("%s: %s" % (c.name, c.tagline))
...
Google: Do No Evil
Apple: AAPL
Yahoo: Internet Company
Django Software Foundation: No Tagline
Since a Func's keyword arguments for __init__() (**extra) and
as_sql() (**extra_context) are interpolated into the SQL string rather
than passed as query parameters (where the database driver would escape them),
they must not contain untrusted user input.
For example, if substring is user-provided, this function is vulnerable to
SQL injection:
from django.db.models import Func
class Position(Func):
function = 'POSITION'
template = "%(function)s('%(substring)s' in %(expressions)s)"
def __init__(self, expression, substring):
# substring=substring is an SQL injection vulnerability!
super().__init__(expression, substring=substring)
This function generates an SQL string without any parameters. Since
substring is passed to super().__init__() as a keyword argument, it's
interpolated into the SQL string before the query is sent to the database.
Here's a corrected rewrite:
class Position(Func):
function = 'POSITION'
arg_joiner = ' IN '
def __init__(self, expression, substring):
super().__init__(substring, expression)
With substring instead passed as a positional argument, it'll be passed as
a parameter in the database query.
If you're using a database backend that uses a different SQL syntax for a certain function, you can add support for it by monkey patching a new method onto the function's class.
Let's say we're writing a backend for Microsoft's SQL Server which uses the SQL
LEN instead of LENGTH for the Length function.
We'll monkey patch a new method called as_sqlserver() onto the Length
class:
from django.db.models.functions import Length
def sqlserver_length(self, compiler, connection):
return self.as_sql(compiler, connection, function='LEN')
Length.as_sqlserver = sqlserver_length
You can also customize the SQL using the template parameter of as_sql().
We use as_sqlserver() because django.db.connection.vendor returns
sqlserver for the backend.
Third-party backends can register their functions in the top level
__init__.py file of the backend package or in a top level expressions.py
file (or package) that is imported from the top level __init__.py.
For user projects wishing to patch the backend that they're using, this code
should live in an AppConfig.ready() method.
2022年6月01日