# Documentação Django 6.0.7

# 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.

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

## 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

A `Manager` is 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.

### 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.

#### `QuerySet`s 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())

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.

#### `QuerySet`s são “lazy”

`QuerySet` objects 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 `QuerySet`s

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 `JOIN`s 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 setting `related_query_name`, 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?

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(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 `QuerySets`s

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 `QuerySet`s correctly. For example, the following will create two `QuerySet`s, 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 `QuerySet`s não são armazenados no “cache”

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)

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, but understand the constraint of no deferred queries before using `defer()`, `only()`, or `fetch_mode()`.

- 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.

### No deferred queries

Relations are deferred by default. If the `blog` relation is not prefetched, accessing `entry.blog` during asynchronous evaluation of a queryset attempts a blocking database query, raising `SynchronousOnlyOperation`. (A similar limitation applies to fields deferred via `defer()` or `only()`.) To avoid this, see the advice on prefetching in Objetos relacionados.

### 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 the `JSONNull()` expression.

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, `isnull=True` is used to match SQL `NULL`, while exact-matching `JSONNull()` is used to match JSON `null`.

Obsoleto desde a versão 6.1: Exact-matching `None` in a query to mean JSON `null` is deprecated. After the deprecation period, it will be interpreted as SQL `NULL`.

Changed in Django 6.1:

`JSONNull()` expression was added.

    >>> from django.db.models import JSONNull
    >>> Dog.objects.create(name="Max", data=None)  # SQL NULL.
    <Dog: Max>
    >>> Dog.objects.create(name="Archie", data=JSONNull())  # JSON null.
    <Dog: Archie>
    >>> Dog.objects.filter(data=None)
    ...: RemovedInDjango70Warning: Using None as the right-hand side of an
    exact lookup on JSONField to mean JSON scalar 'null' is deprecated. Use
    JSONNull() instead (or use the __isnull lookup if you meant SQL NULL).
    ...
    <QuerySet [<Dog: Archie>]>
    >>> Dog.objects.filter(data=JSONNull())
    <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`.

Storing JSON `null` inside JSON data

While `JSONNull()` can be used in `jsonfield.key` exact lookups, it cannot be stored inside `dict` or `list` instances meant to be saved in a `JSONField`, unless a custom encoder is used. If you don’t want to use a custom encoder, use `None` instead.

### 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>]>

To query a key for JSON `null`, `None` or `JSONNull()` can be used.

    >>> Dog.objects.filter(data__owner=None)
    <Dog: Meg>
    >>> Dog.objects.filter(data__owner=JSONNull())
    <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 is a negative integer, it cannot be used in a filter keyword directly, but you can still use dictionary unpacking to use it in a query:

    >>> Dog.objects.filter(**{"data__owner__other_pets__-1__name": "Fishy"})
    <QuerySet [<Dog: Rufus>]>

MySQL, MariaDB, and Oracle < 21c

Negative JSON array indices are not supported.

Changed in Django 6.0:

SQLite support for negative JSON array indices was added.

Changed in Django 6.1:

Oracle 21c+ support for negative JSON array indices was added.

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`

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.

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()

Note that it is not possible to copy instances of models with deferred fields using this pattern unless values are assigned to them:

    >>> blog = Blog.objects.defer("name")[0]
    >>> blog.pk = None
    >>> blog._state.adding = True
    >>> blog.save()
    Traceback (most recent call last):
        ...
    AttributeError: Cannot retrieve deferred field 'name' from an unsaved model.
    >>> blog.name = "Another Blog"
    >>> blog.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 (with `ForeignKey`, `OneToOneField`, or `ManyToManyField`), instances of the model class gain accessor attributes for the related object(s).

Using the models at the top of this page, for example, an `Entry` object `e` has its associated `Blog` object accessible in its `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 a manager that returns all related `Entry` objects in the `entry_set` attribute: `b.entry_set.all()`.

These accessors may be prefetched by the `QuerySet` methods `select_related()` or `prefetch_related()`. If not prefetched, access will trigger an on-demand fetch through the model’s fetch mode.

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 `QuerySet` instances, 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 ``QuerySet`` instances.
    >>> 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])

#### Filtering on many-to-many relationships

When calling `filter()` on a many-to-many relationship, be aware that the join between `Entry` and the intermediary model to `Author` is performed only once, resulting in a restrictive, or “sticky”, filter. Consider the following example:

    >>> from datetime import date
    >>> batucada = Blog.objects.create(name="Batucada Blog")
    >>> e = Entry.objects.create(
    ...     blog=batucada,
    ...     headline="Supporting social movements with drums",
    ...     pub_date=date(2019, 6, 14),
    ... )

    >>> gloria = Author.objects.create(name="Gloria")
    >>> anna = Author.objects.create(name="Anna")
    >>> e.authors.add(gloria, anna)

    >>> anna.entry_set.filter(authors__name="Gloria")
    <QuerySet []>

This filtered query is looking for blog entries that are co-authored by `anna` and `gloria`. You would expect it to return the entry `e`. However, the filter condition, which traverses the many-to-many relationship between `Entry` and `Author`, yields an empty `QuerySet`.

Since the join between `Entry` and the intermediary model to `Author` happens only once, no single object of the joined models - i.e., a relation between one author and one entry - can fulfill the query condition (entries that are co-authored by `anna` and `gloria`). You can circumvent this behavior by chaining two consecutive `filter()` calls, resulting in two separate joins and thus a more permissive filter:

    >>> anna.entry_set.filter().filter(authors__name="Gloria")
    <QuerySet [<Entry: Supporting social movements with drums>]>

exclude() is also sticky

Please note that for this example, `exclude()` behaves similarly to `filter()` despite being implemented differently. When traversing the many-to-many relationship, it does not exclude the entry `e` despite being co-authored by Gloria:

    >>> anna.entry_set.exclude(authors__name="Gloria")
    <QuerySet [<Entry: Supporting social movements with drums>]>

When chaining a second `exclude()` call, an empty `QuerySet` is returned, as expected:

    >>> anna.entry_set.exclude().exclude(authors__name="Gloria")
    <QuerySet []>

However, in other cases, `exclude()` behaves differently from `filter()`. See the note in the “Spanning multi-valued relationships” section above.

### 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 track 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.