Django2.0手册:Making queries

Once you’ve created your data models, Django
automatically gives you a database-abstraction API that lets you create,
retrieve, update and delete objects. This document explains how to use this
API. Refer to the data model reference for full
details of all the various model lookup options.

Throughout this guide (and in the reference), we’ll refer to the following
models, which comprise a Weblog application:

from django.db import models

class Blog(models.Model):
    name = models.CharField(max_length=100)
    tagline = models.TextField()

    def __str__(self):
        return self.name

class Author(models.Model):
    name = models.CharField(max_length=200)
    email = models.EmailField()

    def __str__(self):
        return self.name

class Entry(models.Model):
    blog = models.ForeignKey(Blog, on_delete=models.CASCADE)
    headline = models.CharField(max_length=255)
    body_text = models.TextField()
    pub_date = models.DateField()
    mod_date = models.DateField()
    authors = models.ManyToManyField(Author)
    n_comments = models.IntegerField()
    n_pingbacks = models.IntegerField()
    rating = models.IntegerField()

    def __str__(self):
        return self.headline

Creating objects¶

To represent database-table data in Python objects, Django uses an intuitive
system: A model class represents a database table, and an instance of that
class represents a particular record in the database table.

To create an object, instantiate it using keyword arguments to the model class,
then call save() to save it to the database.

Assuming models live in a file mysite/blog/models.py, here’s an example:

>>> from blog.models import Blog
>>> b = Blog(name='Beatles Blog', tagline='All the latest Beatles news.')
>>> b.save()

This performs an INSERT SQL statement behind the scenes. Django doesn’t hit
the database until you explicitly call save().

The save() method has no return value.

See also

save() takes a number of advanced options not
described here. See the documentation for
save() for complete details.

To create and save an object in a single step, use the
create() method.

Saving changes to objects¶

To save changes to an object that’s already in the database, use
save().

Given a Blog instance b5 that has already been saved to the database,
this example changes its name and updates its record in the database:

>>> b5.name = 'New name'
>>> b5.save()

This performs an UPDATE SQL statement behind the scenes. Django doesn’t hit
the database until you explicitly call save().

Saving ForeignKey and ManyToManyField fields¶

Updating a ForeignKey field works exactly the same
way as saving a normal field — simply assign an object of the right type to
the field in question. This example updates the blog attribute of an
Entry instance entry, assuming appropriate instances of Entry and
Blog are already saved to the database (so we can retrieve them below):

>>> from blog.models import Blog, Entry
>>> entry = Entry.objects.get(pk=1)
>>> cheese_blog = Blog.objects.get(name="Cheddar Talk")
>>> entry.blog = cheese_blog
>>> entry.save()

Updating a ManyToManyField works a little
differently — use the
add() method on the field
to add a record to the relation. This example adds the Author instance
joe to the entry object:

>>> from blog.models import Author
>>> joe = Author.objects.create(name="Joe")
>>> entry.authors.add(joe)

To add multiple records to a ManyToManyField in one
go, include multiple arguments in the call to
add(), like this:

>>> john = Author.objects.create(name="John")
>>> paul = Author.objects.create(name="Paul")
>>> george = Author.objects.create(name="George")
>>> ringo = Author.objects.create(name="Ringo")
>>> entry.authors.add(john, paul, george, ringo)

Django will complain if you try to assign or add an object of the wrong type.

Retrieving objects¶

To retrieve objects from your database, construct a
QuerySet via a
Manager on your model class.

A QuerySet represents a collection of objects
from your database. It can have zero, one or many filters. Filters narrow
down the query results based on the given parameters. In SQL terms, a
QuerySet equates to a SELECT statement,
and a filter is a limiting clause such as WHERE or LIMIT.

You get a QuerySet by using your model’s
Manager. Each model has at least one
Manager, and it’s called
objects by default. Access it directly via the
model class, like so:

>>> Blog.objects
<django.db.models.manager.Manager object at ...>
>>> b = Blog(name='Foo', tagline='Bar')
>>> b.objects
Traceback:
    ...
AttributeError: "Manager isn't accessible via Blog instances."

Note

Managers are accessible only via model classes, rather than from model
instances, to enforce a separation between “table-level” operations and
“record-level” operations.

The Manager is the main source of QuerySets for
a model. For example, Blog.objects.all() returns a
QuerySet that contains all Blog objects in
the database.

Retrieving all objects¶

The simplest way to retrieve objects from a table is to get all of them. To do
this, use the all() method on a
Manager:

>>> all_entries = Entry.objects.all()

The all() method returns a
QuerySet of all the objects in the database.

Retrieving specific objects with filters¶

The QuerySet returned by
all() describes all objects in the
database table. Usually, though, you’ll need to select only a subset of the
complete set of objects.

To create such a subset, you refine the initial
QuerySet, adding filter conditions. The two
most common ways to refine a QuerySet are:

filter(**kwargs)
Returns a new QuerySet containing objects
that match the given lookup parameters.
exclude(**kwargs)
Returns a new QuerySet containing objects
that do not match the given lookup parameters.

The lookup parameters (**kwargs in the above function definitions) should
be in the format described in Field lookups below.

For example, to get a QuerySet of blog entries
from the year 2006, use filter() like
so:

Entry.objects.filter(pub_date__year=2006)

With the default manager class, it is the same as:

Entry.objects.all().filter(pub_date__year=2006)

Chaining filters

The result of refining a QuerySet is itself a
QuerySet, so it’s possible to chain
refinements together. For example:

>>> Entry.objects.filter(
...     headline__startswith='What'
... ).exclude(
...     pub_date__gte=datetime.date.today()
... ).filter(
...     pub_date__gte=datetime.date(2005, 1, 30)
... )

This takes the initial QuerySet of all entries
in the database, adds a filter, then an exclusion, then another filter. The
final result is a QuerySet containing all
entries with a headline that starts with “What”, that were published between
January 30, 2005, and the current day.

Filtered QuerySets are unique

Each time you refine a QuerySet, you get a
brand-new QuerySet that is in no way bound to
the previous QuerySet. Each refinement creates
a separate and distinct QuerySet that can be
stored, used and reused.

举例:

>>> q1 = Entry.objects.filter(headline__startswith="What")
>>> q2 = q1.exclude(pub_date__gte=datetime.date.today())
>>> q3 = q1.filter(pub_date__gte=datetime.date.today())

These three QuerySets are separate. The first is a base
QuerySet containing all entries that contain a
headline starting with “What”. The second is a subset of the first, with an
additional criteria that excludes records whose pub_date is today or in the
future. The third is a subset of the first, with an additional criteria that
selects only the records whose pub_date is today or in the future. The
initial QuerySet (q1) is unaffected by the
refinement process.

QuerySets are lazy

QuerySets are lazy — the act of creating a
QuerySet doesn’t involve any database
activity. You can stack filters together all day long, and Django won’t
actually run the query until the QuerySet is
evaluated. Take a look at this example:

>>> q = Entry.objects.filter(headline__startswith="What")
>>> q = q.filter(pub_date__lte=datetime.date.today())
>>> q = q.exclude(body_text__icontains="food")
>>> print(q)

Though this looks like three database hits, in fact it hits the database only
once, at the last line (print(q)). In general, the results of a
QuerySet aren’t fetched from the database
until you “ask” for them. When you do, the
QuerySet is evaluated by accessing the
database. For more details on exactly when evaluation takes place, see
When QuerySets are evaluated.

Retrieving a single object with get()¶

filter() will always give you a
QuerySet, even if only a single object matches
the query – in this case, it will be a
QuerySet containing a single element.

If you know there is only one object that matches your query, you can use the
get() method on a
Manager which returns the object directly:

>>> one_entry = Entry.objects.get(pk=1)

You can use any query expression with
get(), just like with
filter() – again, see Field lookups
below.

Note that there is a difference between using
get(), and using
filter() with a slice of [0]. If
there are no results that match the query,
get() will raise a DoesNotExist
exception. This exception is an attribute of the model class that the query is
being performed on – so in the code above, if there is no Entry object with
a primary key of 1, Django will raise Entry.DoesNotExist.

Similarly, Django will complain if more than one item matches the
get() query. In this case, it will raise
MultipleObjectsReturned, which again is an
attribute of the model class itself.

Other QuerySet methods¶

Most of the time you’ll use all(),
get(),
filter() and
exclude() when you need to look up
objects from the database. However, that’s far from all there is; see the
QuerySet API Reference for a complete list of all the
various QuerySet methods.

Limiting QuerySets¶

Use a subset of Python’s array-slicing syntax to limit your
QuerySet to a certain number of results. This
is the equivalent of SQL’s LIMIT and OFFSET clauses.

For example, this returns the first 5 objects (LIMIT 5):

>>> Entry.objects.all()[:5]

This returns the sixth through tenth objects (OFFSET 5 LIMIT 5):

>>> Entry.objects.all()[5:10]

Negative indexing (i.e. Entry.objects.all()[-1]) is not supported.

Generally, slicing a QuerySet returns a new
QuerySet — it doesn’t evaluate the query. An
exception is if you use the “step” parameter of Python slice syntax. For
example, this would actually execute the query in order to return a list of
every second object of the first 10:

>>> Entry.objects.all()[:10:2]

Further filtering or ordering of a sliced queryset is prohibited due to the
ambiguous nature of how that might work.

To retrieve a single object rather than a list
(e.g. SELECT foo FROM bar LIMIT 1), use a simple index instead of a
slice. For example, this returns the first Entry in the database, after
ordering entries alphabetically by headline:

>>> Entry.objects.order_by('headline')[0]

This is roughly equivalent to:

>>> Entry.objects.order_by('headline')[0:1].get()

Note, however, that the first of these will raise IndexError while the
second will raise DoesNotExist if no objects match the given criteria. See
get() for more details.

Field lookups¶

Field lookups are how you specify the meat of an SQL WHERE clause. They’re
specified as keyword arguments to the QuerySet
methods filter(),
exclude() and
get().

Basic lookups keyword arguments take the form field__lookuptype=value.
(That’s a double-underscore). For example:

>>> Entry.objects.filter(pub_date__lte='2006-01-01')

translates (roughly) into the following SQL:

SELECT * FROM blog_entry WHERE pub_date <= '2006-01-01';

How this is possible

Python has the ability to define functions that accept arbitrary name-value
arguments whose names and values are evaluated at runtime. For more
information, see Keyword Arguments in the official Python tutorial.

The field specified in a lookup has to be the name of a model field. There’s
one exception though, in case of a ForeignKey you
can specify the field name suffixed with _id. In this case, the value
parameter is expected to contain the raw value of the foreign model’s primary
key. For example:

>>> Entry.objects.filter(blog_id=4)

If you pass an invalid keyword argument, a lookup function will raise
TypeError.

The database API supports about two dozen lookup types; a complete reference
can be found in the field lookup reference. To give you
a taste of what’s available, here’s some of the more common lookups you’ll
probably use:

exact

An “exact” match. For example:

>>> Entry.objects.get(headline__exact="Cat bites dog")

Would generate SQL along these lines:

SELECT ... WHERE headline = 'Cat bites dog';

If you don’t provide a lookup type — that is, if your keyword argument
doesn’t contain a double underscore — the lookup type is assumed to be
exact.

For example, the following two statements are equivalent:

>>> Blog.objects.get(id__exact=14)  # Explicit form
>>> Blog.objects.get(id=14)         # __exact is implied

This is for convenience, because exact lookups are the common case.

iexact

A case-insensitive match. So, the query:

>>> Blog.objects.get(name__iexact="beatles blog")

Would match a Blog titled "Beatles Blog", "beatles blog", or
even "BeAtlES blOG".

contains

Case-sensitive containment test. For example:

Entry.objects.get(headline__contains='Lennon')

Roughly translates to this SQL:

SELECT ... WHERE headline LIKE '%Lennon%';

Note this will match the headline 'Today Lennon honored' but not
'today lennon honored'.

There’s also a case-insensitive version, icontains.

startswith, endswith
Starts-with and ends-with search, respectively. There are also
case-insensitive versions called istartswith and
iendswith.

Again, this only scratches the surface. A complete reference can be found in the
field lookup reference.

Lookups that span relationships¶

Django offers a powerful and intuitive way to “follow” relationships in
lookups, taking care of the SQL JOINs for you automatically, behind the
scenes. To span a relationship, just use the field name of related fields
across models, separated by double underscores, until you get to the field you
want.

This example retrieves all Entry objects with a Blog whose name
is 'Beatles Blog':

>>> Entry.objects.filter(blog__name='Beatles Blog')

This spanning can be as deep as you’d like.

It works backwards, too. To refer to a “reverse” relationship, just use the
lowercase name of the model.

This example retrieves all Blog objects which have at least one Entry
whose headline contains 'Lennon':

>>> Blog.objects.filter(entry__headline__contains='Lennon')

If you are filtering across multiple relationships and one of the intermediate
models doesn’t have a value that meets the filter condition, Django will treat
it as if there is an empty (all values are NULL), but valid, object there.
All this means is that no error will be raised. For example, in this filter:

Blog.objects.filter(entry__authors__name='Lennon')

(if there was a related Author model), if there was no author
associated with an entry, it would be treated as if there was also no name
attached, rather than raising an error because of the missing author.
Usually this is exactly what you want to have happen. The only case where it
might be confusing is if you are using isnull. Thus:

Blog.objects.filter(entry__authors__name__isnull=True)

will return Blog objects that have an empty name on the author and
also those which have an empty author on the entry. If you don’t want
those latter objects, you could write:

Blog.objects.filter(entry__authors__isnull=False, entry__authors__name__isnull=True)

Spanning multi-valued relationships

When you are filtering an object based on a
ManyToManyField or a reverse
ForeignKey, there are two different sorts of filter
you may be interested in. Consider the Blog/Entry relationship
(Blog to Entry is a one-to-many relation). We might be interested in
finding blogs that have an entry which has both “Lennon” in the headline and
was published in 2008. Or we might want to find blogs that have an entry with
“Lennon” in the headline as well as an entry that was published
in 2008. Since there are multiple entries associated with a single Blog,
both of these queries are possible and make sense in some situations.

The same type of situation arises with a
ManyToManyField. For example, if an Entry has a
ManyToManyField called tags, we might want to
find entries linked to tags called “music” and “bands” or we might want an
entry that contains a tag with a name of “music” and a status of “public”.

To handle both of these situations, Django has a consistent way of processing
filter() calls. Everything inside a
single filter() call is applied
simultaneously to filter out items matching all those requirements. Successive
filter() calls further restrict the set
of objects, but for multi-valued relations, they apply to any object linked to
the primary model, not necessarily those objects that were selected by an
earlier filter() call.

That may sound a bit confusing, so hopefully an example will clarify. To
select all blogs that contain entries with both “Lennon” in the headline
and that were published in 2008 (the same entry satisfying both conditions),
we would write:

Blog.objects.filter(entry__headline__contains='Lennon', entry__pub_date__year=2008)

To select all blogs that contain an entry with “Lennon” in the headline
as well as an entry that was published in 2008, we would write:

Blog.objects.filter(entry__headline__contains='Lennon').filter(entry__pub_date__year=2008)

Suppose there is only one blog that had both entries containing “Lennon” and
entries from 2008, but that none of the entries from 2008 contained “Lennon”.
The first query would not return any blogs, but the second query would return
that one blog.

In the second example, the first filter restricts the queryset to all those
blogs linked to entries with “Lennon” in the headline. The second filter
restricts the set of blogs further to those that are also linked to entries
that were published in 2008. The entries selected by the second filter may or
may not be the same as the entries in the first filter. We are filtering the
Blog items with each filter statement, not the Entry items.

Note

The behavior of filter() for queries
that span multi-value relationships, as described above, is not implemented
equivalently for exclude(). Instead,
the conditions in a single exclude()
call will not necessarily refer to the same item.

For example, the following query would exclude blogs that contain both
entries with “Lennon” in the headline and entries published in 2008:

Blog.objects.exclude(
    entry__headline__contains='Lennon',
    entry__pub_date__year=2008,
)

However, unlike the behavior when using
filter(), this will not limit blogs
based on entries that satisfy both conditions. In order to do that, i.e.
to select all blogs that do not contain entries published with “Lennon”
that were published in 2008, you need to make two queries:

Blog.objects.exclude(
    entry__in=Entry.objects.filter(
        headline__contains='Lennon',
        pub_date__year=2008,
    ),
)

Filters can reference fields on the model¶

In the examples given so far, we have constructed filters that compare
the value of a model field with a constant. But what if you want to compare
the value of a model field with another field on the same model?

Django provides F expressions to allow such
comparisons. Instances of F() act as a reference to a model field within a
query. These references can then be used in query filters to compare the values
of two different fields on the same model instance.

For example, to find a list of all blog entries that have had more comments
than pingbacks, we construct an F() object to reference the pingback count,
and use that F() object in the query:

>>> from django.db.models import F
>>> Entry.objects.filter(n_comments__gt=F('n_pingbacks'))

Django supports the use of addition, subtraction, multiplication,
division, modulo, and power arithmetic with F() objects, both with constants
and with other F() objects. To find all the blog entries with more than
twice as many comments as pingbacks, we modify the query:

>>> Entry.objects.filter(n_comments__gt=F('n_pingbacks') * 2)

To find all the entries where the rating of the entry is less than the
sum of the pingback count and comment count, we would issue the
query:

>>> Entry.objects.filter(rating__lt=F('n_comments') + F('n_pingbacks'))

You can also use the double underscore notation to span relationships in
an F() object. An F() object with a double underscore will introduce
any joins needed to access the related object. For example, to retrieve all
the entries where the author’s name is the same as the blog name, we could
issue the query:

>>> Entry.objects.filter(authors__name=F('blog__name'))

For date and date/time fields, you can add or subtract a
timedelta object. The following would return all entries
that were modified more than 3 days after they were published:

>>> from datetime import timedelta
>>> Entry.objects.filter(mod_date__gt=F('pub_date') + timedelta(days=3))

The F() objects support bitwise operations by .bitand(), .bitor(),
.bitrightshift(), and .bitleftshift(). For example:

>>> F('somefield').bitand(16)
Changed in Django 1.11:

Support for .bitrightshift() and .bitleftshift() was added.

The pk lookup shortcut¶

For convenience, Django provides a pk lookup shortcut, which stands for
“primary key”.

In the example Blog model, the primary key is the id field, so these
three statements are equivalent:

>>> Blog.objects.get(id__exact=14) # Explicit form
>>> Blog.objects.get(id=14) # __exact is implied
>>> Blog.objects.get(pk=14) # pk implies id__exact

The use of pk isn’t limited to __exact queries — any query term
can be combined with pk to perform a query on the primary key of a model:

# Get blogs entries with id 1, 4 and 7
>>> Blog.objects.filter(pk__in=[1,4,7])

# Get all blog entries with id > 14
>>> Blog.objects.filter(pk__gt=14)

pk lookups also work across joins. For example, these three statements are
equivalent:

>>> Entry.objects.filter(blog__id__exact=3) # Explicit form
>>> Entry.objects.filter(blog__id=3)        # __exact is implied
>>> Entry.objects.filter(blog__pk=3)        # __pk implies __id__exact

Escaping percent signs and underscores in LIKE statements¶

The field lookups that equate to LIKE SQL statements (iexact,
contains, icontains, startswith, istartswith, endswith
and iendswith) will automatically escape the two special characters used in
LIKE statements — the percent sign and the underscore. (In a LIKE
statement, the percent sign signifies a multiple-character wildcard and the
underscore signifies a single-character wildcard.)

This means things should work intuitively, so the abstraction doesn’t leak.
For example, to retrieve all the entries that contain a percent sign, just use
the percent sign as any other character:

>>> Entry.objects.filter(headline__contains='%')

Django takes care of the quoting for you; the resulting SQL will look something
like this:

SELECT ... WHERE headline LIKE '%\%%';

Same goes for underscores. Both percentage signs and underscores are handled
for you transparently.

Caching and QuerySets¶

Each QuerySet contains a cache to minimize
database access. Understanding how it works will allow you to write the most
efficient code.

In a newly created QuerySet, the cache is
empty. The first time a QuerySet is evaluated
— and, hence, a database query happens — Django saves the query results in
the QuerySet’s cache and returns the results
that have been explicitly requested (e.g., the next element, if the
QuerySet is being iterated over). Subsequent
evaluations of the QuerySet reuse the cached
results.

Keep this caching behavior in mind, because it may bite you if you don’t use
your QuerySets correctly. For example, the
following will create two QuerySets, evaluate
them, and throw them away:

>>> print([e.headline for e in Entry.objects.all()])
>>> print([e.pub_date for e in Entry.objects.all()])

That means the same database query will be executed twice, effectively doubling
your database load. Also, there’s a possibility the two lists may not include
the same database records, because an Entry may have been added or deleted
in the split second between the two requests.

To avoid this problem, simply save the
QuerySet and reuse it:

>>> queryset = Entry.objects.all()
>>> print([p.headline for p in queryset]) # Evaluate the query set.
>>> print([p.pub_date for p in queryset]) # Re-use the cache from the evaluation.

When QuerySets are not cached

Querysets do not always cache their results. When evaluating only part of
the queryset, the cache is checked, but if it is not populated then the items
returned by the subsequent query are not cached. Specifically, this means that
limiting the queryset using an array slice or an
index will not populate the cache.

For example, repeatedly getting a certain index in a queryset object will query
the database each time:

>>> queryset = Entry.objects.all()
>>> print(queryset[5]) # Queries the database
>>> print(queryset[5]) # Queries the database again

However, if the entire queryset has already been evaluated, the cache will be
checked instead:

>>> queryset = Entry.objects.all()
>>> [entry for entry in queryset] # Queries the database
>>> print(queryset[5]) # Uses cache
>>> print(queryset[5]) # Uses cache

Here are some examples of other actions that will result in the entire queryset
being evaluated and therefore populate the cache:

>>> [entry for entry in queryset]
>>> bool(queryset)
>>> entry in queryset
>>> list(queryset)

Note

Simply printing the queryset will not populate the cache. This is because
the call to __repr__() only returns a slice of the entire queryset.

Complex lookups with Q objects¶

Keyword argument queries — in filter(),
etc. — are “AND”ed together. If you need to execute more complex queries (for
example, queries with OR statements), you can use Q objects.

A Q object (django.db.models.Q) is an object
used to encapsulate a collection of keyword arguments. These keyword arguments
are specified as in “Field lookups” above.

For example, this Q object encapsulates a single LIKE query:

from django.db.models import Q
Q(question__startswith='What')

Q objects can be combined using the & and | operators. When an
operator is used on two Q objects, it yields a new Q object.

For example, this statement yields a single Q object that represents the
“OR” of two "question__startswith" queries:

Q(question__startswith='Who') | Q(question__startswith='What')

This is equivalent to the following SQL WHERE clause:

WHERE question LIKE 'Who%' OR question LIKE 'What%'

You can compose statements of arbitrary complexity by combining Q objects
with the & and | operators and use parenthetical grouping. Also, Q
objects can be negated using the ~ operator, allowing for combined lookups
that combine both a normal query and a negated (NOT) query:

Q(question__startswith='Who') | ~Q(pub_date__year=2005)

Each lookup function that takes keyword-arguments
(e.g. filter(),
exclude(),
get()) can also be passed one or more
Q objects as positional (not-named) arguments. If you provide multiple
Q object arguments to a lookup function, the arguments will be “AND”ed
together. For example:

Poll.objects.get(
    Q(question__startswith='Who'),
    Q(pub_date=date(2005, 5, 2)) | Q(pub_date=date(2005, 5, 6))
)

… roughly translates into the SQL:

SELECT * from polls WHERE question LIKE 'Who%'
    AND (pub_date = '2005-05-02' OR pub_date = '2005-05-06')

Lookup functions can mix the use of Q objects and keyword arguments. All
arguments provided to a lookup function (be they keyword arguments or Q
objects) are “AND”ed together. However, if a Q object is provided, it must
precede the definition of any keyword arguments. For example:

Poll.objects.get(
    Q(pub_date=date(2005, 5, 2)) | Q(pub_date=date(2005, 5, 6)),
    question__startswith='Who',
)

… would be a valid query, equivalent to the previous example; but:

# INVALID QUERY
Poll.objects.get(
    question__startswith='Who',
    Q(pub_date=date(2005, 5, 2)) | Q(pub_date=date(2005, 5, 6))
)

… would not be valid.

See also

The OR lookups examples in the Django unit tests show some possible uses
of Q.

Comparing objects¶

To compare two model instances, just use the standard Python comparison operator,
the double equals sign: ==. Behind the scenes, that compares the primary
key values of two models.

Using the Entry example above, the following two statements are equivalent:

>>> some_entry == other_entry
>>> some_entry.id == other_entry.id

If a model’s primary key isn’t called id, no problem. Comparisons will
always use the primary key, whatever it’s called. For example, if a model’s
primary key field is called name, these two statements are equivalent:

>>> some_obj == other_obj
>>> some_obj.name == other_obj.name

Deleting objects¶

The delete method, conveniently, is named
delete(). This method immediately deletes the
object and returns the number of objects deleted and a dictionary with
the number of deletions per object type. Example:

>>> e.delete()
(1, {'weblog.Entry': 1})

You can also delete objects in bulk. Every
QuerySet has a
delete() method, which deletes all
members of that QuerySet.

For example, this deletes all Entry objects with a pub_date year of
2005:

>>> Entry.objects.filter(pub_date__year=2005).delete()
(5, {'webapp.Entry': 5})

Keep in mind that this will, whenever possible, be executed purely in SQL, and
so the delete() methods of individual object instances will not necessarily
be called during the process. If you’ve provided a custom delete() method
on a model class and want to ensure that it is called, you will need to
“manually” delete instances of that model (e.g., by iterating over a
QuerySet and calling delete() on each
object individually) rather than using the bulk
delete() method of a
QuerySet.

When Django deletes an object, by default it emulates the behavior of the SQL
constraint ON DELETE CASCADE — in other words, any objects which had
foreign keys pointing at the object to be deleted will be deleted along with
it. For example:

b = Blog.objects.get(pk=1)
# This will delete the Blog and all of its Entry objects.
b.delete()

This cascade behavior is customizable via the
on_delete argument to the
ForeignKey.

Note that delete() is the only
QuerySet method that is not exposed on a
Manager itself. This is a safety mechanism to
prevent you from accidentally requesting Entry.objects.delete(), and
deleting all the entries. If you do want to delete all the objects, then
you have to explicitly request a complete query set:

Entry.objects.all().delete()

Copying model instances¶

Although there is no built-in method for copying model instances, it is
possible to easily create new instance with all fields’ values copied. In the
simplest case, you can just set pk to None. Using our blog example:

blog = Blog(name='My blog', tagline='Blogging is easy')
blog.save() # blog.pk == 1

blog.pk = None
blog.save() # blog.pk == 2

Things get more complicated if you use inheritance. Consider a subclass of
Blog:

class ThemeBlog(Blog):
    theme = models.CharField(max_length=200)

django_blog = ThemeBlog(name='Django', tagline='Django is easy', theme='python')
django_blog.save() # django_blog.pk == 3

Due to how inheritance works, you have to set both pk and id to None:

django_blog.pk = None
django_blog.id = None
django_blog.save() # django_blog.pk == 4

This process doesn’t copy relations that aren’t part of the model’s database
table. For example, Entry has a ManyToManyField to Author. After
duplicating an entry, you must set the many-to-many relations for the new
entry:

entry = Entry.objects.all()[0] # some previous entry
old_authors = entry.authors.all()
entry.pk = None
entry.save()
entry.authors.set(old_authors)

For a OneToOneField, you must duplicate the related object and assign it
to the new object’s field to avoid violating the one-to-one unique constraint.
For example, assuming entry is already duplicated as above:

detail = EntryDetail.objects.all()[0]
detail.pk = None
detail.entry = entry
detail.save()

Updating multiple objects at once¶

Sometimes you want to set a field to a particular value for all the objects in
a QuerySet. You can do this with the
update() method. For example:

# Update all the headlines with pub_date in 2007.
Entry.objects.filter(pub_date__year=2007).update(headline='Everything is the same')

You can only set non-relation fields and ForeignKey
fields using this method. To update a non-relation field, provide the new value
as a constant. To update ForeignKey fields, set the
new value to be the new model instance you want to point to. For example:

>>> b = Blog.objects.get(pk=1)

# Change every Entry so that it belongs to this Blog.
>>> Entry.objects.all().update(blog=b)

The update() method is applied instantly and returns the number of rows
matched by the query (which may not be equal to the number of rows updated if
some rows already have the new value). The only restriction on the
QuerySet being updated is that it can only
access one database table: the model’s main table. You can filter based on
related fields, but you can only update columns in the model’s main
table. Example:

>>> b = Blog.objects.get(pk=1)

# Update all the headlines belonging to this Blog.
>>> Entry.objects.select_related().filter(blog=b).update(headline='Everything is the same')

Be aware that the update() method is converted directly to an SQL
statement. It is a bulk operation for direct updates. It doesn’t run any
save() methods on your models, or emit the
pre_save or post_save signals (which are a consequence of calling
save()), or honor the
auto_now field option.
If you want to save every item in a QuerySet
and make sure that the save() method is called on
each instance, you don’t need any special function to handle that. Just loop
over them and call save():

for item in my_queryset:
    item.save()

Calls to update can also use F expressions to
update one field based on the value of another field in the model. This is
especially useful for incrementing counters based upon their current value. For
example, to increment the pingback count for every entry in the blog:

>>> Entry.objects.all().update(n_pingbacks=F('n_pingbacks') + 1)

However, unlike F() objects in filter and exclude clauses, you can’t
introduce joins when you use F() objects in an update — you can only
reference fields local to the model being updated. If you attempt to introduce
a join with an F() object, a FieldError will be raised:

# This will raise a FieldError
>>> Entry.objects.update(headline=F('blog__name'))

Falling back to raw SQL¶

If you find yourself needing to write an SQL query that is too complex for
Django’s database-mapper to handle, you can fall back on writing SQL by hand.
Django has a couple of options for writing raw SQL queries; see
Performing raw SQL queries.

Finally, it’s important to note that the Django database layer is merely an
interface to your database. You can access your database via other tools,
programming languages or database frameworks; there’s nothing Django-specific
about your database.