Fazendo consultas

Uma vez que tenha criado seu modelos de dados, o Django automaticamente lhe dá uma API de abstração do banco de dados que deixa que crie, retorne, edite e delete objetos. Este documento explica como usar essa API. Refira-se a Referência de modelo de dados para detalhes completos de todos os vários modelos de opções de filtros.

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

from datetime import date

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(default=date.today)
    authors = models.ManyToManyField(Author)
    number_of_comments = models.IntegerField(default=0)
    number_of_pingbacks = models.IntegerField(default=0)
    rating = models.IntegerField(default=5)

    def __str__(self):
        return self.headline

Criando objetos

Para representar uma tabela de banco de dados em objetos Python, o Django usa um sistema intuitivo: Uma classe de modelo que representa uma tabela de banco de dados, e uma intância desta classe representa um registro particular em uma tabela de banco de dados.

Para criar um objeto, instancie-o usando argumentos nomeados para a classe de modelo, então chame o save() para persistí-lo no banco de dados.

Assuming models live in a models.py file inside a blog Django app, here is an example:

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

Este executa um comando SQL INSERT por detrás dos panos. O Django não acessa o banco de dados até que você chame explicitamente o save().

O método save() não retorna um valor .

Ver também

O save() recebe várias opções avançadas não descritas aqui. Veja a documentacão em save() para detalhes completos.

Para criar e salvar um objeto em um único passo, use o método create().

Salvando alterações para objetos

Para salvar as alerações para um objeto que já existe no banco de dados, use o 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()

Isso executa um comando SQL UPDATE por detras dos panos. o Django não acessa o banco de dados até que você explicitamente chame o save().

Salvando campos ForeignKey e ManyToManyField

Updating a ForeignKey field works exactly the same way as saving a normal field – 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)

O Django irá reclamar se você tentar assinalar ou adicionar um objeto do tipo errado.

Recuperando objetos

Para recuperar objetos do seu banco de dados, construa uma QuerySet através da Manager na sua classe de modelo.

A QuerySet representa uma coleção de objetos do seu banco de dados. Ele pode ter zero, um ou muitos * filtros*. Filtros limitam os resultados baseado nos parâmetros dados. Em termos de SQL, um QuerySet equivale a um comando SELECT, e um filtro é um clásula limitante tal como 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."

Nota

Os Managers são acessíveis somente através das classes de modelo, e não de instâncias de modelos, para reforçar a separação entre operações no “nível das tabelas” e operações no “nível dos registros”.

A Manager é a principal fonte de QuerySets para um modelo. Por exemplo, Blog.objects.all() retorna uma QuerySet que contém todos os objetos do tipo Blog do banco de dados.

Recuperando todos os objetos

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()

O método all() retorna uma QuerySet de todos os objetos do banco de dados.

Recuperando objetos específicos com filtros.

A QuerySet retornada pelo all() descreve todos os objetos da tabela do banco de dados. Em geral, porém, você precisa selecionar somente um subconjunto de todo o conjunto de objetos.

Para criar o subconjunto, você refina o QuerySet inicial, adicionando filtros de condições. As duas maneiras mais comuns de refinar um QuerySet são:

filter(**kwargs)

Retorna uma nova QuerySet contendo objetos que combinem com os parâmetros de filtros dados.

exclude(**kwargs)

Retornam uma nova QuerySet contendo objetos que não combinem com os parâmetros de filtros dados.

Os parâmetros de filtros (**kwargs nas definições da função acima) devem estar no formato descrito em Filtros de campo abaixo.

Prr exemplo, para ter um QuerySet de entradas de blog do ano 2006, use o filter() como aqui:

Entry.objects.filter(pub_date__year=2006)

Com a classe “manager” padrão, é o mesmo que:

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

Filtros encadeados

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))

Ele pega o QuerySet inicial com todas as entradas do banco de dados, adiciona um filtro, então adiciona uma exclusão, então outro filtro. O resultado final é um QuerySet contendo todas as entradas com uma manchete que comece com “What”, que foi publicada entre 30 de janeiro de 2005 e o dia de hoje.

QuerySets filtradas são únicas

Cada vez que refine um QuerySet, você tem uma nova QuerySet que não está de forma alguma vinculada ao anterior QuerySet. Cada refinamento cria uma QuerySet separada e distinta que pode ser armazenada, usada e resusada.

Example:

>>> 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())

Estes três QuerySets são separados. O primeiro é um QuerySet básico contendo todas as entradas que contenham uma manchete iniciando com “What”. O segundo é um subconjunto do primeiro, com um critério adicional que exclui aqueles cujo o pub_date é hoje ou está no futuro. O terceiro é também um subconjunto do primeiro, com um critério adicional que seleciona somente os registros cujo o pub_date é hoje ou está no futuro. A QuerySet (q1) inicial não é afetado pelo processo de refinamento.

QuerySets são “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)

Apesar de parece que isso seja três acessos ao banco de dados, de fato ele acessa o banco de dados somente um vez, na última linha (print(q)). Em geral, os resultados de uma QuerySet não são buscados no banco de dados até que você “peça” por eles. Quando fizer, a QuerySet é interpretada acessando o banco de dados. Para mais detalhes de quando exatamente a interpretação ocorre, veja When QuerySets are evaluated.

Recuperando um único objeto com get()

O filter() sempre lhe dará um QuerySet, mesmo se um único objeto combina com a consulta - neste caso, ele será uma QuerySet que contém um único elemento.

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)

Você pode usar qualquer expressão de consulta com get(), tal como com filter() - denovo, veja o Campos de consulta abaixo.

Note que existe uma diferença entre usar o get(), e usar o filter() com uma fatia de [0]. Se não houver resultados que combinem com a consulta, o get() irá emitir uma exceção DoesNotExist. Esta exceção é um atributo da classe de modelo na qual a consulta está sendo realizada - no código acima, se não houver objeto Entry com a chave-primária de 1, o Django irá emitir um Entry.DoesNotExist.

De maneira similar, o Django irá reclamar se mais de um item combinar com a consulta get(). Neste caso, ele emitirá um MultipleObjectsReturned, o qual denovo é ele próprio um atributo da classe de modelo.

Outros QuerySet métodos

Na maioria das vezes você usará o all(), get(), filter() e o exclude() quando precisar buscar objetos no banco de dados. Porém, está longe do todo que existe; veja the a Refrêcia da API de QuerySet para uma lista completa das vários métodos da QuerySet.

Limitando QuerySets

Use um subconjunto da syntax de fatias de array Python para limitar seu QuerySet para um certo número de resultados. Este é o equivalente às cláusulas SQL Limit e OFFSET.

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]

Índice negativo (isto é Entry.objects.all()[-1]) não é suportado.

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 an 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, porém, que o primeiro irá emitir um IndexError enquanto o segundo emitirá um DoesNotExist se nenhum objeto combinar com o critério dado. Veja o get() para mais detalhes.

Filtros de campo

Filtros de campo é o que você usa para especiíicar os parâmetros da cláusula WHERE. Eles são especificados como argumentos nomeados para os métodos da QuerySet: filter(), exclude() e 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")

Traduzido (groseiramente) no seguinte SQL:

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

Como isso é possível

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.

O campo especificado em um filtro tem que ser um nome de campoo do modelo. Existe uma exceção porém, no caso de uma ForeignKey você pode especificar o nome do campo com um sufixo _id. Neste caso, é esperado que o valor do parâmetro contenha literalmente o valor da chave-primária do modelo estrageiro. Por exemplo:

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

Se você passar um argumento nomeado inválido, a funçao do filtro irá emitir um 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")

Geraria SQL ao longo destas linhas:

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

Se você não fornecer um tipo de filtro – isto é, se o seu argumento nomeado não contiver um “underscore” duplo – o tipo de filtro é assumido como sendo 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

Isso se dá por conveniência, porque os filtros exact são casos comuns.

iexact

A case-insensitive match. So, the query:

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

Deveria encontrar um Blog entitulado "Beatles Blog", "beatles blog", ou mesmo "BeAtlES blOG".

contains

Teste de contenção sensíveis ao tipo de caixa. Por exemplo:

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

Mais ou menos traduzido para este SQL:

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

Note que este irá encontrar o “headline” 'Today Lennon honored' mas não o 'today lennon honored'.

Existe também uma versão que ignora o tipo de caixa, icontains.

startswith, endswith

Busca começa-com e termina-com, respectivamente. Existe também a versão que ignora o tipo de caixa chamada istartswith e iendswith.

Denovo, isso aqui somente arranha a superfície. Uma refrência completa pode ser achada na Referência de filtros de campo.

Filtros que abrangem os relacionamentos

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, 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")

A abrangância pode ser tão profunda quanto queira.

It works backwards, too. While it can be customized, by default you refer to a “reverse” relationship in a lookup using 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")

Se você estiver filtrando através de múltiplos relacionamentos e um dos modelos intermediários não tiver um valor que vá de encontro com a condição do filtro, o Django irá tratá-lo como se houvesse um objeto vazio (todos os valores são NULL), mas válido. Tudo isso significa que nenhum erro será emitido. Por exemplo, neste filtro:

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

(se houvesse um modelo Author relacionado), se não houvesse author associado com uma “entry”, seria tratado como se também não houvesse um name anexo, ao invés de emitir um erro por causa do author faltante.

Blog.objects.filter(entry__authors__name__isnull=True)

irá retornar objeto do tipo Blog que tenham um name vazio no author a também aqueles os quais tem um author vazio no entry. Se você não que estes últimos objetos, você poderia escrever:

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

Abrangendo relacionamentos multi-interpretados

When spanning a ManyToManyField or a reverse ForeignKey (such as from Blog to Entry), filtering on multiple attributes raises the question of whether to require each attribute to coincide in the same related object. We might seek blogs that have an entry from 2008 with “Lennon” in its headline, or we might seek blogs that merely have any entry from 2008 as well as some newer or older entry with “Lennon” in its headline.

To select all blogs containing at least one entry from 2008 having “Lennon” in its headline (the same entry satisfying both conditions), we would write:

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

Otherwise, to perform a more permissive query selecting any blogs with merely some entry with “Lennon” in its headline and some entry from 2008, we would write:

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

Suppose there is only one blog that has 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. (This is because 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.) In short, if each condition needs to match the same related object, then each should be contained in a single filter() call.

Nota

As the second (more permissive) query chains multiple filters, it performs multiple joins to the primary model, potentially yielding duplicates.

>>> from datetime import date
>>> beatles = Blog.objects.create(name="Beatles Blog")
>>> pop = Blog.objects.create(name="Pop Music Blog")
>>> Entry.objects.create(
...     blog=beatles,
...     headline="New Lennon Biography",
...     pub_date=date(2008, 6, 1),
... )
<Entry: New Lennon Biography>
>>> Entry.objects.create(
...     blog=beatles,
...     headline="New Lennon Biography in Paperback",
...     pub_date=date(2009, 6, 1),
... )
<Entry: New Lennon Biography in Paperback>
>>> Entry.objects.create(
...     blog=pop,
...     headline="Best Albums of 2008",
...     pub_date=date(2008, 12, 15),
... )
<Entry: Best Albums of 2008>
>>> Entry.objects.create(
...     blog=pop,
...     headline="Lennon Would Have Loved Hip Hop",
...     pub_date=date(2020, 4, 1),
... )
<Entry: Lennon Would Have Loved Hip Hop>
>>> Blog.objects.filter(
...     entry__headline__contains="Lennon",
...     entry__pub_date__year=2008,
... )
<QuerySet [<Blog: Beatles Blog>]>
>>> Blog.objects.filter(
...     entry__headline__contains="Lennon",
... ).filter(
...     entry__pub_date__year=2008,
... )
<QuerySet [<Blog: Beatles Blog>, <Blog: Beatles Blog>, <Blog: Pop Music Blog]>

Nota

O comportamento do filter() para consultas que abrangem relacionamentos com valores múltiplos, como descrito acima, não é implementado de maneira equivalente no exclude(). Ao invés, as condições em uma única chamada exclude() não necessariamente irão se referenciar ao mesmo item.

Por exemplo, a seguinte consulta excluiria blogs que contém ambas “entries” com “Lennon” na manchete e “entries” publicadas em 2008:

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

Contudo, diferente do comportamento quando usado o filter(), este não limitará blogs baseados em “entries” que satisfaçam ambas as condições. Para tal, isto é, para selecionar todos os blogs que não contenham “entries” publicadas com “Lennon” que foram publicadas em 2008, você precisa fazer duas consultas:

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

Filtros podem referenciar campos do modelo

Nos exemplos dados até agora, construímos filtros que comparam o valor de um campo de modelo com uma constante. Mas e se você quiser comparar o valor de um modelo com outro campo no mesmo modelo?

O Django fornece a F expressions para permitir tais comparações. Instâncias de F() atuam como uma referência a um campo de modelo dentro de uma consulta. Essas referências podem então ser comparadas a valores de dois diferentes campos na mesma instância de modelo.

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(number_of_comments__gt=F("number_of_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(number_of_comments__gt=F("number_of_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("number_of_comments") + F("number_of_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(), .bitxor(), .bitrightshift(), and .bitleftshift(). For example:

>>> F("somefield").bitand(16)

Oracle

Oracle doesn’t support bitwise XOR operation.

Expressions can reference transforms

Django supports using transforms in expressions.

For example, to find all Entry objects published in the same year as they were last modified:

>>> from django.db.models import F
>>> Entry.objects.filter(pub_date__year=F("mod_date__year"))

To find the earliest year an entry was published, we can issue the query:

>>> from django.db.models import Min
>>> Entry.objects.aggregate(first_published_year=Min("pub_date__year"))

This example finds the value of the highest rated entry and the total number of comments on all entries for each year:

>>> from django.db.models import OuterRef, Subquery, Sum
>>> Entry.objects.values("pub_date__year").annotate(
...     top_rating=Subquery(
...         Entry.objects.filter(
...             pub_date__year=OuterRef("pub_date__year"),
...         )
...         .order_by("-rating")
...         .values("rating")[:1]
...     ),
...     total_comments=Sum("number_of_comments"),
... )

O atalho de filtro pk

Por conveniência, o Django fornece um atalho para o filtro pk, o que representa a “chave-primária”.

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

Substituição de sinais de porcentagem e “underscores” nos comandos Like

Os campos filtros que equivalem ao comando SQL LIKE (iexact, contains, icontains, startswith, istartswith, endswith and iendswith) irão automaticamente substituir os dois caracteres especiais usados em comandos LIKE – o sinal de porcentagem e o “underscore”. (Em um comando LIKE, o sinal de porcentagem significa múltiplos-caracteres curingas e o “underscore” siginifica um único caracter curinga.)

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

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

O Django irá cuidar da citação por você; o SQL resultante se parecerá com algo como:

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

O mesmo vale para os “underscores”. Ambos os sinais, porcentagem e “underscores”, são manipulados para você de maneira transparente.

“Cache” e QuerySetss

Cada QuerySet contém um “cache” para minimizar o acesso ao banco de dados. Entendendo como isso funciona lhe permitirá escrever código mais eficiente.

Em uma classe QuerySet que acaba de ser criada, o “cache” está vazio. A primeira vez que a QuerySet é interpretada – e portanto, uma consulta ao banco acontece – o Django salva o resultado da consulta no “cache” da QuerySet’s e retorna o resultado que foi requerido explicitamente (exemplo, o próximo elemento, se o QuerySet está sendo iterado). Execuçãoes subsequentes do QuerySet reusam os resultados que estão no “cache”.

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()])

Isso significa que a mesma consulta de banco de dados será executada duas vezes, efetivamente dobrando a carga no banco de dados. Também existe a posibilidade das duas listas não incluírem os mesmos registros de banco de dados, porque uma Entry talvez tenha sido adicionada ou deletada durante a fração de segundo entre as duas requisições.

To avoid this problem, 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])  # Reuse the cache from the evaluation.

Quando QuerySets não são armazenados no “cache”

Resultados de consultas nem sempre são salvas no “cache”. Quando interpretar somente parte da consulta, o “cache” é veririficado, mas se ele não estiver populado então os itens retornados pela consulta subsequente não vão para o “cache”. Especificamente, isso significa que limitar consultas usando o fatiamento de “array” ou um índice não irá popular o 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)

Nota

Simplesmente dar um “print” no “queryset” não popula o “cache”. Isso é porque a chamada do ``__repr__()``somente retorna uma fatia de todo o “queryset”.

Asynchronous queries

If you are writing asynchronous views or code, you cannot use the ORM for queries in quite the way we have described above, as you cannot call blocking synchronous code from asynchronous code - it will block up the event loop (or, more likely, Django will notice and raise a SynchronousOnlyOperation to stop that from happening).

Fortunately, you can do many queries using Django’s asynchronous query APIs. Every method that might block - such as get() or delete() - has an asynchronous variant (aget() or adelete()), and when you iterate over results, you can use asynchronous iteration (async for) instead.

Query iteration

The default way of iterating over a query - with for - will result in a blocking database query behind the scenes as Django loads the results at iteration time. To fix this, you can swap to async for:

async for entry in Authors.objects.filter(name__startswith="A"):
    ...

Be aware that you also can’t do other things that might iterate over the queryset, such as wrapping list() around it to force its evaluation (you can use async for in a comprehension, if you want it).

Because QuerySet methods like filter() and exclude() do not actually run the query - they set up the queryset to run when it’s iterated over - you can use those freely in asynchronous code. For a guide to which methods can keep being used like this, and which have asynchronous versions, read the next section.

QuerySet and manager methods

Some methods on managers and querysets - like get() and first() - force execution of the queryset and are blocking. Some, like filter() and exclude(), don’t force execution and so are safe to run from asynchronous code. But how are you supposed to tell the difference?

While you could poke around and see if there is an a-prefixed version of the method (for example, we have aget() but not afilter()), there is a more logical way - look up what kind of method it is in the QuerySet reference.

In there, you’ll find the methods on QuerySets grouped into two sections:

• Methods that return new querysets: These are the non-blocking ones, and don’t have asynchronous versions. You’re free to use these in any situation, though read the notes on defer() and only() before you use them.

• Methods that do not return querysets: These are the blocking ones, and have asynchronous versions - the asynchronous name for each is noted in its documentation, though our standard pattern is to add an a prefix.

Using this distinction, you can work out when you need to use asynchronous versions, and when you don’t. For example, here’s a valid asynchronous query:

user = await User.objects.filter(username=my_input).afirst()

filter() returns a queryset, and so it’s fine to keep chaining it inside an asynchronous environment, whereas first() evaluates and returns a model instance - thus, we change to afirst(), and use await at the front of the whole expression in order to call it in an asynchronous-friendly way.

Nota

If you forget to put the await part in, you may see errors like “coroutine object has no attribute x” or “<coroutine …>” strings in place of your model instances. If you ever see these, you are missing an await somewhere to turn that coroutine into a real value.

Transações

Transactions are not currently supported with asynchronous queries and updates. You will find that trying to use one raises SynchronousOnlyOperation.

If you wish to use a transaction, we suggest you write your ORM code inside a separate, synchronous function and then call that using sync_to_async - see Suporte assíncrono for more.

Querying JSONField

Lookups implementation is different in JSONField, mainly due to the existence of key transformations. To demonstrate, we will use the following example model:

from django.db import models


class Dog(models.Model):
    name = models.CharField(max_length=200)
    data = models.JSONField(null=True)

    def __str__(self):
        return self.name

Storing and querying for None

As with other fields, storing None as the field’s value will store it as SQL NULL. While not recommended, it is possible to store JSON scalar null instead of SQL NULL by using Value(None, JSONField()).

Whichever of the values is stored, when retrieved from the database, the Python representation of the JSON scalar null is the same as SQL NULL, i.e. None. Therefore, it can be hard to distinguish between them.

This only applies to None as the top-level value of the field. If None is inside a list or dict, it will always be interpreted as JSON null.

When querying, None value will always be interpreted as JSON null. To query for SQL NULL, use isnull:

>>> Dog.objects.create(name="Max", data=None)  # SQL NULL.
<Dog: Max>
>>> Dog.objects.create(name="Archie", data=Value(None, JSONField()))  # JSON null.
<Dog: Archie>
>>> Dog.objects.filter(data=None)
<QuerySet [<Dog: Archie>]>
>>> Dog.objects.filter(data=Value(None, JSONField()))
<QuerySet [<Dog: Archie>]>
>>> Dog.objects.filter(data__isnull=True)
<QuerySet [<Dog: Max>]>
>>> Dog.objects.filter(data__isnull=False)
<QuerySet [<Dog: Archie>]>

Unless you are sure you wish to work with SQL NULL values, consider setting null=False and providing a suitable default for empty values, such as default=dict.

Nota

Storing JSON scalar null does not violate null=False.

Key, index, and path transforms

To query based on a given dictionary key, use that key as the lookup name:

>>> Dog.objects.create(
...     name="Rufus",
...     data={
...         "breed": "labrador",
...         "owner": {
...             "name": "Bob",
...             "other_pets": [
...                 {
...                     "name": "Fishy",
...                 }
...             ],
...         },
...     },
... )
<Dog: Rufus>
>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": None})
<Dog: Meg>
>>> Dog.objects.filter(data__breed="collie")
<QuerySet [<Dog: Meg>]>

Multiple keys can be chained together to form a path lookup:

>>> Dog.objects.filter(data__owner__name="Bob")
<QuerySet [<Dog: Rufus>]>

If the key is an integer, it will be interpreted as an index transform in an array:

>>> Dog.objects.filter(data__owner__other_pets__0__name="Fishy")
<QuerySet [<Dog: Rufus>]>

If the key you wish to query by clashes with the name of another lookup, use the contains lookup instead.

To query for missing keys, use the isnull lookup:

>>> Dog.objects.create(name="Shep", data={"breed": "collie"})
<Dog: Shep>
>>> Dog.objects.filter(data__owner__isnull=True)
<QuerySet [<Dog: Shep>]>

Nota

The lookup examples given above implicitly use the exact lookup. Key, index, and path transforms can also be chained with: icontains, endswith, iendswith, iexact, regex, iregex, startswith, istartswith, lt, lte, gt, and gte, as well as with Containment and key lookups.

KT() expressions

class KT(lookup)

Represents the text value of a key, index, or path transform of JSONField. You can use the double underscore notation in lookup to chain dictionary key and index transforms.

Por exemplo:

>>> from django.db.models.fields.json import KT
>>> Dog.objects.create(
...     name="Shep",
...     data={
...         "owner": {"name": "Bob"},
...         "breed": ["collie", "lhasa apso"],
...     },
... )
<Dog: Shep>
>>> Dog.objects.annotate(
...     first_breed=KT("data__breed__1"), owner_name=KT("data__owner__name")
... ).filter(first_breed__startswith="lhasa", owner_name="Bob")
<QuerySet [<Dog: Shep>]>

Nota

Due to the way in which key-path queries work, exclude() and filter() are not guaranteed to produce exhaustive sets. If you want to include objects that do not have the path, add the isnull lookup.

Aviso

Since any string could be a key in a JSON object, any lookup other than those listed below will be interpreted as a key lookup. No errors are raised. Be extra careful for typing mistakes, and always check your queries work as you intend.

MariaDB and Oracle users

Using order_by() on key, index, or path transforms will sort the objects using the string representation of the values. This is because MariaDB and Oracle Database do not provide a function that converts JSON values into their equivalent SQL values.

Oracle users

On Oracle Database, using None as the lookup value in an exclude() query will return objects that do not have null as the value at the given path, including objects that do not have the path. On other database backends, the query will return objects that have the path and the value is not null.

PostgreSQL users

On PostgreSQL, if only one key or index is used, the SQL operator -> is used. If multiple operators are used then the #> operator is used.

SQLite users

On SQLite, "true", "false", and "null" string values will always be interpreted as True, False, and JSON null respectively.

Containment and key lookups

contains

The contains lookup is overridden on JSONField. The returned objects are those where the given dict of key-value pairs are all contained in the top-level of the field. For example:

>>> Dog.objects.create(name="Rufus", data={"breed": "labrador", "owner": "Bob"})
<Dog: Rufus>
>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})
<Dog: Meg>
>>> Dog.objects.create(name="Fred", data={})
<Dog: Fred>
>>> Dog.objects.create(
...     name="Merry", data={"breed": "pekingese", "tricks": ["fetch", "dance"]}
... )
>>> Dog.objects.filter(data__contains={"owner": "Bob"})
<QuerySet [<Dog: Rufus>, <Dog: Meg>]>
>>> Dog.objects.filter(data__contains={"breed": "collie"})
<QuerySet [<Dog: Meg>]>
>>> Dog.objects.filter(data__contains={"tricks": ["dance"]})
<QuerySet [<Dog: Merry>]>

Oracle and SQLite

contains is not supported on Oracle and SQLite.

contained_by

This is the inverse of the contains lookup - the objects returned will be those where the key-value pairs on the object are a subset of those in the value passed. For example:

>>> Dog.objects.create(name="Rufus", data={"breed": "labrador", "owner": "Bob"})
<Dog: Rufus>
>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})
<Dog: Meg>
>>> Dog.objects.create(name="Fred", data={})
<Dog: Fred>
>>> Dog.objects.create(
...     name="Merry", data={"breed": "pekingese", "tricks": ["fetch", "dance"]}
... )
>>> Dog.objects.filter(data__contained_by={"breed": "collie", "owner": "Bob"})
<QuerySet [<Dog: Meg>, <Dog: Fred>]>
>>> Dog.objects.filter(data__contained_by={"breed": "collie"})
<QuerySet [<Dog: Fred>]>
>>> Dog.objects.filter(
...     data__contained_by={"breed": "pekingese", "tricks": ["dance", "fetch", "hug"]}
... )
<QuerySet [<Dog: Merry>, <Dog: Fred>]>

Oracle and SQLite

contained_by is not supported on Oracle and SQLite.

has_key

Returns objects where the given key is in the top-level of the data. For example:

>>> Dog.objects.create(name="Rufus", data={"breed": "labrador"})
<Dog: Rufus>
>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})
<Dog: Meg>
>>> Dog.objects.filter(data__has_key="owner")
<QuerySet [<Dog: Meg>]>

has_keys

Returns objects where all of the given keys are in the top-level of the data. For example:

>>> Dog.objects.create(name="Rufus", data={"breed": "labrador"})
<Dog: Rufus>
>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})
<Dog: Meg>
>>> Dog.objects.filter(data__has_keys=["breed", "owner"])
<QuerySet [<Dog: Meg>]>

has_any_keys

Returns objects where any of the given keys are in the top-level of the data. For example:

>>> Dog.objects.create(name="Rufus", data={"breed": "labrador"})
<Dog: Rufus>
>>> Dog.objects.create(name="Meg", data={"owner": "Bob"})
<Dog: Meg>
>>> Dog.objects.filter(data__has_any_keys=["owner", "breed"])
<QuerySet [<Dog: Rufus>, <Dog: Meg>]>

Consultas complexas com objetos Q

Consultas com argumentos nomeados – no filter(), etc. – compõem uma “E”. Se você precisa executar consultas mais complexas (por exemplo, consultas com comandos OR), você pode usar Q objects.

Um objeto Q (django.db.models.Q) é um objeto usado para encapsular uma coleção de argumentos nomeados. Estes argumentos são especificados como um “campo de filtro” acima.

Por exemplo, este objeto Q encapsula uma única consulta LIKE:

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.

Por exemplo, este comando produz um único objeto Q que representa o OR de duas consultas "question__startswith":

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)

Para cada função de filtro que recebe argumentos nominados (ex.: filter(), exclude(), get()) pode também se passado um ou mais objetos ``Q``como argumentos posicionais (not-named). Se você prover múltiplos objetos ``Q``para uma função filtro, os argumentos serão interpolados com lógicas “E”. Por exemplo:

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')

Funções de filtro podem misturar o uso de objetos Q e argumentos nomeados. Todos os argumentos fornecidos para uma função filtro (sejam eles argumentos nomeados ou objetos Q) são interpolados com “E”. Porém, se um objeto Q é fornecido, é necessário que este preceda qualquer argumento nomeado. Por exemplo:

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

… seria uma consulta válida, equivalente ao exemplo anterior; mas:

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

… não seria válido.

Ver também

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

Comparando objetos

To compare two model instances, 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

Deletando objetos

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, {'blog.Entry': 1})

Você também pode deletar objetos em massa. Cada QuerySet tem um método delete(), o qual deleta todos os membros daquele 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()

Copiando as instâncias de modelo

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 set pk to None and _state.adding to True. Using our blog example:

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

blog.pk = None
blog._state.adding = True
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, and _state.adding to True:

django_blog.pk = None
django_blog.id = None
django_blog._state.adding = True
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._state.adding = True
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._state.adding = True
detail.entry = entry
detail.save()

Alterando múltiplos objetos de uma só vez.

Algumas vezes você querer definir um campo para um particular valor para todos os objetos em uma :classe:`~django.db.models.query.QuerySet`. Você pode fazer isto com o :método:`~django.db.models.query.QuerySet.update` método. Por exemplo:

# 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.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.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. 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.update(number_of_pingbacks=F("number_of_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"))

Objetos relacionados

When you define a relationship in a model (i.e., a ForeignKey, OneToOneField, or ManyToManyField), instances of that model will have a convenient API to access the related object(s).

Using the models at the top of this page, for example, an Entry object e can get its associated Blog object by accessing the blog attribute: e.blog.

(Behind the scenes, this functionality is implemented by Python descriptors. This shouldn’t really matter to you, but we point it out here for the curious.)

Django also creates API accessors for the “other” side of the relationship – the link from the related model to the model that defines the relationship. For example, a Blog object b has access to a list of all related Entry objects via the entry_set attribute: b.entry_set.all().

All examples in this section use the sample Blog, Author and Entry models defined at the top of this page.

Relações One-to-many

Adiante

If a model has a ForeignKey, instances of that model will have access to the related (foreign) object via an attribute of the model.

Example:

>>> e = Entry.objects.get(id=2)
>>> e.blog  # Returns the related Blog object.

You can get and set via a foreign-key attribute. As you may expect, changes to the foreign key aren’t saved to the database until you call save(). Example:

>>> e = Entry.objects.get(id=2)
>>> e.blog = some_blog
>>> e.save()

If a ForeignKey field has null=True set (i.e., it allows NULL values), you can assign None to remove the relation. Example:

>>> e = Entry.objects.get(id=2)
>>> e.blog = None
>>> e.save()  # "UPDATE blog_entry SET blog_id = NULL ...;"

Forward access to one-to-many relationships is cached the first time the related object is accessed. Subsequent accesses to the foreign key on the same object instance are cached. Example:

>>> e = Entry.objects.get(id=2)
>>> print(e.blog)  # Hits the database to retrieve the associated Blog.
>>> print(e.blog)  # Doesn't hit the database; uses cached version.

Note that the select_related() QuerySet method recursively prepopulates the cache of all one-to-many relationships ahead of time. Example:

>>> e = Entry.objects.select_related().get(id=2)
>>> print(e.blog)  # Doesn't hit the database; uses cached version.
>>> print(e.blog)  # Doesn't hit the database; uses cached version.

Following relationships “backward”

If a model has a ForeignKey, instances of the foreign-key model will have access to a Manager that returns all instances of the first model. By default, this Manager is named FOO_set, where FOO is the source model name, lowercased. This Manager returns QuerySets, which can be filtered and manipulated as described in the “Retrieving objects” section above.

Example:

>>> b = Blog.objects.get(id=1)
>>> b.entry_set.all()  # Returns all Entry objects related to Blog.

# b.entry_set is a Manager that returns QuerySets.
>>> b.entry_set.filter(headline__contains="Lennon")
>>> b.entry_set.count()

You can override the FOO_set name by setting the related_name parameter in the ForeignKey definition. For example, if the Entry model was altered to blog = ForeignKey(Blog, on_delete=models.CASCADE, related_name='entries'), the above example code would look like this:

>>> b = Blog.objects.get(id=1)
>>> b.entries.all()  # Returns all Entry objects related to Blog.

# b.entries is a Manager that returns QuerySets.
>>> b.entries.filter(headline__contains="Lennon")
>>> b.entries.count()

Using a custom reverse manager

By default the RelatedManager used for reverse relations is a subclass of the default manager for that model. If you would like to specify a different manager for a given query you can use the following syntax:

from django.db import models


class Entry(models.Model):
    # ...
    objects = models.Manager()  # Default Manager
    entries = EntryManager()  # Custom Manager


b = Blog.objects.get(id=1)
b.entry_set(manager="entries").all()

If EntryManager performed default filtering in its get_queryset() method, that filtering would apply to the all() call.

Specifying a custom reverse manager also enables you to call its custom methods:

b.entry_set(manager="entries").is_published()

Interaction with prefetching

When calling prefetch_related() with a reverse relation, the default manager will be used. If you want to prefetch related objects using a custom reverse manager, use Prefetch(). For example:

from django.db.models import Prefetch

prefetch_manager = Prefetch("entry_set", queryset=Entry.entries.all())
Blog.objects.prefetch_related(prefetch_manager)

Additional methods to handle related objects

In addition to the QuerySet methods defined in “Retrieving objects” above, the ForeignKey Manager has additional methods used to handle the set of related objects. A synopsis of each is below, and complete details can be found in the related objects reference.

add(obj1, obj2, ...)

Adds the specified model objects to the related object set.

create(**kwargs)

Creates a new object, saves it and puts it in the related object set. Returns the newly created object.

remove(obj1, obj2, ...)

Removes the specified model objects from the related object set.

clear()

Removes all objects from the related object set.

set(objs)

Replace the set of related objects.

To assign the members of a related set, use the set() method with an iterable of object instances. For example, if e1 and e2 are Entry instances:

b = Blog.objects.get(id=1)
b.entry_set.set([e1, e2])

If the clear() method is available, any preexisting objects will be removed from the entry_set before all objects in the iterable (in this case, a list) are added to the set. If the clear() method is not available, all objects in the iterable will be added without removing any existing elements.

Each “reverse” operation described in this section has an immediate effect on the database. Every addition, creation and deletion is immediately and automatically saved to the database.

Many-to-many relationships

Both ends of a many-to-many relationship get automatic API access to the other end. The API works similar to a “backward” one-to-many relationship, above.

One difference is in the attribute naming: The model that defines the ManyToManyField uses the attribute name of that field itself, whereas the “reverse” model uses the lowercased model name of the original model, plus '_set' (just like reverse one-to-many relationships).

An example makes this easier to understand:

e = Entry.objects.get(id=3)
e.authors.all()  # Returns all Author objects for this Entry.
e.authors.count()
e.authors.filter(name__contains="John")

a = Author.objects.get(id=5)
a.entry_set.all()  # Returns all Entry objects for this Author.

Like ForeignKey, ManyToManyField can specify related_name. In the above example, if the ManyToManyField in Entry had specified related_name='entries', then each Author instance would have an entries attribute instead of entry_set.

Another difference from one-to-many relationships is that in addition to model instances, the add(), set(), and remove() methods on many-to-many relationships accept primary key values. For example, if e1 and e2 are Entry instances, then these set() calls work identically:

a = Author.objects.get(id=5)
a.entry_set.set([e1, e2])
a.entry_set.set([e1.pk, e2.pk])

One-to-one relationships

One-to-one relationships are very similar to many-to-one relationships. If you define a OneToOneField on your model, instances of that model will have access to the related object via an attribute of the model.

Por exemplo:

class EntryDetail(models.Model):
    entry = models.OneToOneField(Entry, on_delete=models.CASCADE)
    details = models.TextField()


ed = EntryDetail.objects.get(id=2)
ed.entry  # Returns the related Entry object.

The difference comes in “reverse” queries. The related model in a one-to-one relationship also has access to a Manager object, but that Manager represents a single object, rather than a collection of objects:

e = Entry.objects.get(id=2)
e.entrydetail  # returns the related EntryDetail object

If no object has been assigned to this relationship, Django will raise a DoesNotExist exception.

Instances can be assigned to the reverse relationship in the same way as you would assign the forward relationship:

e.entrydetail = ed

How are the backward relationships possible?

Other object-relational mappers require you to define relationships on both sides. The Django developers believe this is a violation of the DRY (Don’t Repeat Yourself) principle, so Django only requires you to define the relationship on one end.

But how is this possible, given that a model class doesn’t know which other model classes are related to it until those other model classes are loaded?

The answer lies in the app registry. When Django starts, it imports each application listed in INSTALLED_APPS, and then the models module inside each application. Whenever a new model class is created, Django adds backward-relationships to any related models. If the related models haven’t been imported yet, Django keeps tracks of the relationships and adds them when the related models eventually are imported.

For this reason, it’s particularly important that all the models you’re using be defined in applications listed in INSTALLED_APPS. Otherwise, backwards relations may not work properly.

Queries over related objects

Queries involving related objects follow the same rules as queries involving normal value fields. When specifying the value for a query to match, you may use either an object instance itself, or the primary key value for the object.

For example, if you have a Blog object b with id=5, the following three queries would be identical:

Entry.objects.filter(blog=b)  # Query using object instance
Entry.objects.filter(blog=b.id)  # Query using id from instance
Entry.objects.filter(blog=5)  # Query using id directly

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.