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Django2.0手册 AI君 125℃

Django offers a wide variety of built-in lookups for
filtering (for example, exact and icontains). This documentation
explains how to write custom lookups and how to alter the working of existing
lookups. For the API references of lookups, see the Lookup API reference.


Let’s start with a simple custom lookup. We will write a custom lookup ne
which works opposite to exact. Author.objects.filter(name__ne='Jack')
will translate to the SQL:

"author"."name" <> 'Jack'

SQL 会自动适配不同的后端, 所以我们不需要对使用不同的数据库担心.

There are two steps to making this work. Firstly we need to implement the
lookup, then we need to tell Django about it. The implementation is quite

from django.db.models import Lookup

class NotEqual(Lookup):
    lookup_name = 'ne'

    def as_sql(self, compiler, connection):
        lhs, lhs_params = self.process_lhs(compiler, connection)
        rhs, rhs_params = self.process_rhs(compiler, connection)
        params = lhs_params + rhs_params
        return '%s <> %s' % (lhs, rhs), params

To register the NotEqual lookup we will just need to call
register_lookup on the field class we want the lookup to be available. In
this case, the lookup makes sense on all Field subclasses, so we register
it with Field directly:

from django.db.models.fields import Field

Lookup registration can also be done using a decorator pattern:

from django.db.models.fields import Field

class NotEqualLookup(Lookup):
    # ...

We can now use foo__ne for any field foo. You will need to ensure that
this registration happens before you try to create any querysets using it. You
could place the implementation in a file, or register the lookup
in the ready() method of an AppConfig.

Taking a closer look at the implementation, the first required attribute is
lookup_name. This allows the ORM to understand how to interpret name__ne
and use NotEqual to generate the SQL. By convention, these names are always
lowercase strings containing only letters, but the only hard requirement is
that it must not contain the string __.

We then need to define the as_sql method. This takes a SQLCompiler
object, called compiler, and the active database connection.
SQLCompiler objects are not documented, but the only thing we need to know
about them is that they have a compile() method which returns a tuple
containing an SQL string, and the parameters to be interpolated into that
string. In most cases, you don’t need to use it directly and can pass it on to
process_lhs() and process_rhs().

A Lookup works against two values, lhs and rhs, standing for
left-hand side and right-hand side. The left-hand side is usually a field
reference, but it can be anything implementing the query expression API. The right-hand is the value given by the user. In the
example Author.objects.filter(name__ne='Jack'), the left-hand side is a
reference to the name field of the Author model, and 'Jack' is the
right-hand side.

We call process_lhs and process_rhs to convert them into the values we
need for SQL using the compiler object described before. These methods
return tuples containing some SQL and the parameters to be interpolated into
that SQL, just as we need to return from our as_sql method. In the above
example, process_lhs returns ('"author"."name"', []) and
process_rhs returns ('"%s"', ['Jack']). In this example there were no
parameters for the left hand side, but this would depend on the object we have,
so we still need to include them in the parameters we return.

Finally we combine the parts into an SQL expression with <>, and supply all
the parameters for the query. We then return a tuple containing the generated
SQL string and the parameters.

A simple transformer example¶

The custom lookup above is great, but in some cases you may want to be able to
chain lookups together. For example, let’s suppose we are building an
application where we want to make use of the abs() operator.
We have an Experiment model which records a start value, end value, and the
change (start – end). We would like to find all experiments where the change
was equal to a certain amount (Experiment.objects.filter(change__abs=27)),
or where it did not exceed a certain amount


This example is somewhat contrived, but it nicely demonstrates the range of
functionality which is possible in a database backend independent manner,
and without duplicating functionality already in Django.

We will start by writing an AbsoluteValue transformer. This will use the SQL
function ABS() to transform the value before comparison:

from django.db.models import Transform

class AbsoluteValue(Transform):
    lookup_name = 'abs'
    function = 'ABS'

下一步, 让我们为其注册 IntrgerField:

from django.db.models import IntegerField

We can now run the queries we had before.
Experiment.objects.filter(change__abs=27) will generate the following SQL:

SELECT ... WHERE ABS("experiments"."change") = 27

By using Transform instead of Lookup it means we are able to chain
further lookups afterwards. So
Experiment.objects.filter(change__abs__lt=27) will generate the following

SELECT ... WHERE ABS("experiments"."change") < 27

Note that in case there is no other lookup specified, Django interprets
change__abs=27 as change__abs__exact=27.

When looking for which lookups are allowable after the Transform has been
applied, Django uses the output_field attribute. We didn’t need to specify
this here as it didn’t change, but supposing we were applying AbsoluteValue
to some field which represents a more complex type (for example a point
relative to an origin, or a complex number) then we may have wanted to specify
that the transform returns a FloatField type for further lookups. This can
be done by adding an output_field attribute to the transform:

from django.db.models import FloatField, Transform

class AbsoluteValue(Transform):
    lookup_name = 'abs'
    function = 'ABS'

    def output_field(self):
        return FloatField()

This ensures that further lookups like abs__lte behave as they would for
a FloatField.

编写一个高效的 abs__lt 查找¶

When using the above written abs lookup, the SQL produced will not use
indexes efficiently in some cases. In particular, when we use
change__abs__lt=27, this is equivalent to change__gt=-27 AND
change__lt=27. (For the lte case we could use the SQL BETWEEN).

因此, 我们希望 Experiment.objects.filter(change__abs__lt=27) 能生成以下 SQL:

SELECT .. WHERE "experiments"."change" < 27 AND "experiments"."change" > -27


from django.db.models import Lookup

class AbsoluteValueLessThan(Lookup):
    lookup_name = 'lt'

    def as_sql(self, compiler, connection):
        lhs, lhs_params = compiler.compile(self.lhs.lhs)
        rhs, rhs_params = self.process_rhs(compiler, connection)
        params = lhs_params + rhs_params + lhs_params + rhs_params
        return '%s < %s AND %s > -%s' % (lhs, rhs, lhs, rhs), params


There are a couple of notable things going on. First, AbsoluteValueLessThan
isn’t calling process_lhs(). Instead it skips the transformation of the
lhs done by AbsoluteValue and uses the original lhs. That is, we
want to get "experiments"."change" not ABS("experiments"."change").
Referring directly to self.lhs.lhs is safe as AbsoluteValueLessThan
can be accessed only from the AbsoluteValue lookup, that is the lhs
is always an instance of AbsoluteValue.

Notice also that as both sides are used multiple times in the query the params
need to contain lhs_params and rhs_params multiple times.

The final query does the inversion (27 to -27) directly in the
database. The reason for doing this is that if the self.rhs is something else
than a plain integer value (for example an F() reference) we can’t do the
transformations in Python.


In fact, most lookups with __abs could be implemented as range queries
like this, and on most database backends it is likely to be more sensible to
do so as you can make use of the indexes. However with PostgreSQL you may
want to add an index on abs(change) which would allow these queries to
be very efficient.

A bilateral transformer example¶

The AbsoluteValue example we discussed previously is a transformation which
applies to the left-hand side of the lookup. There may be some cases where you
want the transformation to be applied to both the left-hand side and the
right-hand side. For instance, if you want to filter a queryset based on the
equality of the left and right-hand side insensitively to some SQL function.

Let’s examine the simple example of case-insensitive transformation here. This
transformation isn’t very useful in practice as Django already comes with a bunch
of built-in case-insensitive lookups, but it will be a nice demonstration of
bilateral transformations in a database-agnostic way.

We define an UpperCase transformer which uses the SQL function UPPER() to
transform the values before comparison. We define
bilateral = True to indicate that
this transformation should apply to both lhs and rhs:

from django.db.models import Transform

class UpperCase(Transform):
    lookup_name = 'upper'
    function = 'UPPER'
    bilateral = True

下一步, 让我们注册它:

from django.db.models import CharField, TextField

现在, 这个 Author.objects.filter(name__upper=”doe”)“查询集会生成一个像这样的不区分大小写的查询:

SELECT ... WHERE UPPER("author"."name") = UPPER('doe')


Sometimes different database vendors require different SQL for the same
operation. For this example we will rewrite a custom implementation for
MySQL for the NotEqual operator. Instead of <> we will be using !=
operator. (Note that in reality almost all databases support both, including
all the official databases supported by Django).

We can change the behavior on a specific backend by creating a subclass of
NotEqual with an as_mysql method:

class MySQLNotEqual(NotEqual):
    def as_mysql(self, compiler, connection):
        lhs, lhs_params = self.process_lhs(compiler, connection)
        rhs, rhs_params = self.process_rhs(compiler, connection)
        params = lhs_params + rhs_params
        return '%s != %s' % (lhs, rhs), params


We can then register it with Field. It takes the place of the original
NotEqual class as it has the same lookup_name.

When compiling a query, Django first looks for as_%s % connection.vendor
methods, and then falls back to as_sql. The vendor names for the in-built
backends are sqlite, postgresql, oracle and mysql.

How Django determines the lookups and transforms which are used¶

In some cases you may wish to dynamically change which Transform or
Lookup is returned based on the name passed in, rather than fixing it. As
an example, you could have a field which stores coordinates or an arbitrary
dimension, and wish to allow a syntax like .filter(coords__x7=4) to return
the objects where the 7th coordinate has value 4. In order to do this, you
would override get_lookup with something like:

class CoordinatesField(Field):
    def get_lookup(self, lookup_name):
        if lookup_name.startswith('x'):
                dimension = int(lookup_name[1:])
            except ValueError:
                return get_coordinate_lookup(dimension)
        return super().get_lookup(lookup_name)

You would then define get_coordinate_lookup appropriately to return a
Lookup subclass which handles the relevant value of dimension.

There is a similarly named method called get_transform(). get_lookup()
should always return a Lookup subclass, and get_transform() a
Transform subclass. It is important to remember that Transform
objects can be further filtered on, and Lookup objects cannot.

When filtering, if there is only one lookup name remaining to be resolved, we
will look for a Lookup. If there are multiple names, it will look for a
Transform. In the situation where there is only one name and a Lookup
is not found, we look for a Transform and then the exact lookup on that
Transform. All call sequences always end with a Lookup. To clarify:

  • .filter(myfield__mylookup) will call myfield.get_lookup('mylookup').
  • .filter(myfield__mytransform__mylookup) will call
    myfield.get_transform('mytransform'), and then
  • .filter(myfield__mytransform) will first call
    myfield.get_lookup('mytransform'), which will fail, so it will fall back
    to calling myfield.get_transform('mytransform') and then

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