---
title: "QuerySet API reference"
version: 3.2
locale: ja
source: https://docs.djangoproject.com/ja/3.2/ref/models/querysets/
canonical: https://djangodocs.dev/ja/3.2/ref/models/querysets/
---
# `QuerySet` API reference

このドキュメントでは、`QuerySet` API の詳細を説明しています。[モデル](/ja/3.2/topics/db/models/) と [データベースクエリ](/ja/3.2/topics/db/queries/) ガイドにある説明を前提としていますので、このドキュメントを読む前にこの 2 つを読んでおいた方がよいでしょう。

このドキュメント全体を通して、[データベースクエリガイド](/ja/3.2/topics/db/queries/) で使った [Weblog モデル例](/ja/3.2/topics/db/queries/#queryset-model-example) を使用します。

## When `QuerySet`s are evaluated

内部的に、 `QuerySet` は実際にデータベースにアクセスすることなく、構築、フィルタ、スライス、そして基本的に他に渡されます。あなたがクエリセットを評価するために何かしない限り、実際のデータベースへのアクセスは発生しません。

あなたは次のような方法で `QuerySet` を評価することができます:

- **イテレーション。** `QuerySet` はイテラブルで、初めてイテレートした時にデータベースのクエリを実行します。たとえば、これはデータベースにある全エントリのヘッドラインを出力するプログラムです:

  ```
  for e in Entry.objects.all():
      print(e.headline)
  ```

  Note: Don't use this if all you want to do is determine if at least one
  result exists. It's more efficient to use [`exists()`](#django.db.models.query.QuerySet.exists).
- **Slicing.** As explained in [QuerySet の要素数を制限する](/ja/3.2/topics/db/queries/#limiting-querysets), a `QuerySet` can
  be sliced, using Python's array-slicing syntax. Slicing an unevaluated
  `QuerySet` usually returns another unevaluated `QuerySet`, but Django
  will execute the database query if you use the "step" parameter of slice
  syntax, and will return a list. Slicing a `QuerySet` that has been
  evaluated also returns a list.

  Also note that even though slicing an unevaluated `QuerySet` returns
  another unevaluated `QuerySet`, modifying it further (e.g., adding
  more filters, or modifying ordering) is not allowed, since that does not
  translate well into SQL and it would not have a clear meaning either.
- **Pickling/Caching.** See the following section for details of what
  is involved when [pickling QuerySets](#pickling-querysets). The important thing for the
  purposes of this section is that the results are read from the database.
- **repr().** A `QuerySet` is evaluated when you call `repr()` on it.
  This is for convenience in the Python interactive interpreter, so you can
  immediately see your results when using the API interactively.
- **len().** A `QuerySet` is evaluated when you call `len()` on it.
  This, as you might expect, returns the length of the result list.

  Note: If you only need to determine the number of records in the set (and
  don't need the actual objects), it's much more efficient to handle a count
  at the database level using SQL's `SELECT COUNT(*)`. Django provides a
  [`count()`](#django.db.models.query.QuerySet.count) method for precisely this reason.
- **list().** Force evaluation of a `QuerySet` by calling `list()` on
  it. For example:

  ```
  entry_list = list(Entry.objects.all())
  ```
- **bool().** Testing a `QuerySet` in a boolean context, such as using
  `bool()`, `or`, `and` or an `if` statement, will cause the query
  to be executed. If there is at least one result, the `QuerySet` is
  `True`, otherwise `False`. For example:

  ```
  if Entry.objects.filter(headline="Test"):
     print("There is at least one Entry with the headline Test")
  ```

  Note: If you only want to determine if at least one result exists (and don't
  need the actual objects), it's more efficient to use [`exists()`](#django.db.models.query.QuerySet.exists).

### Pickling `QuerySet`s

If you [`pickle`](https://docs.python.org/3/library/pickle.html#module-pickle) a `QuerySet`, this will force all the results to be loaded
into memory prior to pickling. Pickling is usually used as a precursor to
caching and when the cached queryset is reloaded, you want the results to
already be present and ready for use (reading from the database can take some
time, defeating the purpose of caching). This means that when you unpickle a
`QuerySet`, it contains the results at the moment it was pickled, rather
than the results that are currently in the database.

If you only want to pickle the necessary information to recreate the
`QuerySet` from the database at a later time, pickle the `query` attribute
of the `QuerySet`. You can then recreate the original `QuerySet` (without
any results loaded) using some code like this:

```
>>> import pickle
>>> query = pickle.loads(s)     # Assuming 's' is the pickled string.
>>> qs = MyModel.objects.all()
>>> qs.query = query            # Restore the original 'query'.
```

The `query` attribute is an opaque object. It represents the internals of
the query construction and is not part of the public API. However, it is safe
(and fully supported) to pickle and unpickle the attribute's contents as
described here.

> **Restrictions on QuerySet.values_list()**
>
> If you recreate [`QuerySet.values_list()`](#django.db.models.query.QuerySet.values_list) using the pickled `query`
> attribute, it will be converted to [`QuerySet.values()`](#django.db.models.query.QuerySet.values):
>
> ```
> >>> import pickle
> >>> qs = Blog.objects.values_list('id', 'name')
> >>> qs
> <QuerySet [(1, 'Beatles Blog')]>
> >>> reloaded_qs = Blog.objects.all()
> >>> reloaded_qs.query = pickle.loads(pickle.dumps(qs.query))
> >>> reloaded_qs
> <QuerySet [{'id': 1, 'name': 'Beatles Blog'}]>
> ```

> **You can't share pickles between versions**
>
> Pickles of `QuerySets` are only valid for the version of Django that
> was used to generate them. If you generate a pickle using Django
> version N, there is no guarantee that pickle will be readable with
> Django version N+1. Pickles should not be used as part of a long-term
> archival strategy.
>
> Since pickle compatibility errors can be difficult to diagnose, such as
> silently corrupted objects, a `RuntimeWarning` is raised when you try to
> unpickle a queryset in a Django version that is different than the one in
> which it was pickled.

## `QuerySet` API

Here's the formal declaration of a `QuerySet`:

#### `class QuerySet(model=None, query=None, using=None, hints=None)`

Usually when you'll interact with a `QuerySet` you'll use it by
[chaining filters](/ja/3.2/topics/db/queries/#chaining-filters). To make this work, most
`QuerySet` methods return new querysets. These methods are covered in
detail later in this section.

The `QuerySet` class has two public attributes you can use for
introspection:

#### `ordered`

`True` if the `QuerySet` is ordered — i.e. has an
[`order_by()`](#django.db.models.query.QuerySet.order_by) clause or a default ordering on the model.
`False` otherwise.

#### `db`

The database that will be used if this query is executed now.

> **Note**
>
> The `query` parameter to [`QuerySet`](#django.db.models.query.QuerySet) exists so that specialized
> query subclasses can reconstruct internal query state. The value of the
> parameter is an opaque representation of that query state and is not
> part of a public API.

### Methods that return new `QuerySet`s

Django provides a range of `QuerySet` refinement methods that modify either
the types of results returned by the `QuerySet` or the way its SQL query is
executed.

#### `filter()`

#### `filter(*args, **kwargs)`

Returns a new `QuerySet` containing objects that match the given lookup
parameters.

The lookup parameters (`**kwargs`) should be in the format described in
[Field lookups](#id4) below. Multiple parameters are joined via `AND` in the
underlying SQL statement.

If you need to execute more complex queries (for example, queries with `OR` statements),
you can use [`Q objects`](#django.db.models.Q) (`*args`).

#### `exclude()`

#### `exclude(*args, **kwargs)`

Returns a new `QuerySet` containing objects that do *not* match the given
lookup parameters.

The lookup parameters (`**kwargs`) should be in the format described in
[Field lookups](#id4) below. Multiple parameters are joined via `AND` in the
underlying SQL statement, and the whole thing is enclosed in a `NOT()`.

This example excludes all entries whose `pub_date` is later than 2005-1-3
AND whose `headline` is "Hello":

```
Entry.objects.exclude(pub_date__gt=datetime.date(2005, 1, 3), headline='Hello')
```

In SQL terms, that evaluates to:

```sql
SELECT ...
WHERE NOT (pub_date > '2005-1-3' AND headline = 'Hello')
```

This example excludes all entries whose `pub_date` is later than 2005-1-3
OR whose headline is "Hello":

```
Entry.objects.exclude(pub_date__gt=datetime.date(2005, 1, 3)).exclude(headline='Hello')
```

In SQL terms, that evaluates to:

```sql
SELECT ...
WHERE NOT pub_date > '2005-1-3'
AND NOT headline = 'Hello'
```

Note the second example is more restrictive.

If you need to execute more complex queries (for example, queries with `OR` statements),
you can use [`Q objects`](#django.db.models.Q) (`*args`).

#### `annotate()`

#### `annotate(*args, **kwargs)`

Annotates each object in the `QuerySet` with the provided list of [query
expressions](/ja/3.2/ref/models/expressions/). An expression may be a simple value, a
reference to a field on the model (or any related models), or an aggregate
expression (averages, sums, etc.) that has been computed over the objects that
are related to the objects in the `QuerySet`.

Each argument to `annotate()` is an annotation that will be added
to each object in the `QuerySet` that is returned.

The aggregation functions that are provided by Django are described
in [Aggregation Functions](#id5) below.

Annotations specified using keyword arguments will use the keyword as
the alias for the annotation. Anonymous arguments will have an alias
generated for them based upon the name of the aggregate function and
the model field that is being aggregated. Only aggregate expressions
that reference a single field can be anonymous arguments. Everything
else must be a keyword argument.

For example, if you were manipulating a list of blogs, you may want
to determine how many entries have been made in each blog:

```
>>> from django.db.models import Count
>>> q = Blog.objects.annotate(Count('entry'))
# The name of the first blog
>>> q[0].name
'Blogasaurus'
# The number of entries on the first blog
>>> q[0].entry__count
42
```

The `Blog` model doesn't define an `entry__count` attribute by itself,
but by using a keyword argument to specify the aggregate function, you can
control the name of the annotation:

```
>>> q = Blog.objects.annotate(number_of_entries=Count('entry'))
# The number of entries on the first blog, using the name provided
>>> q[0].number_of_entries
42
```

For an in-depth discussion of aggregation, see [the topic guide on
Aggregation](/ja/3.2/topics/db/aggregation/).

#### `alias()`

#### `alias(*args, **kwargs)`

> **New in Django 3.2**

Same as [`annotate()`](#django.db.models.query.QuerySet.annotate), but instead of annotating objects in the
`QuerySet`, saves the expression for later reuse with other `QuerySet`
methods. This is useful when the result of the expression itself is not needed
but it is used for filtering, ordering, or as a part of a complex expression.
Not selecting the unused value removes redundant work from the database which
should result in better performance.

For example, if you want to find blogs with more than 5 entries, but are not
interested in the exact number of entries, you could do this:

```
>>> from django.db.models import Count
>>> blogs = Blog.objects.alias(entries=Count('entry')).filter(entries__gt=5)
```

`alias()` can be used in conjunction with [`annotate()`](#django.db.models.query.QuerySet.annotate), [`exclude()`](#django.db.models.query.QuerySet.exclude),
[`filter()`](#django.db.models.query.QuerySet.filter), [`order_by()`](#django.db.models.query.QuerySet.order_by), and [`update()`](#django.db.models.query.QuerySet.update). To use aliased expression
with other methods (e.g. [`aggregate()`](#django.db.models.query.QuerySet.aggregate)), you must promote it to an
annotation:

```
Blog.objects.alias(entries=Count('entry')).annotate(
    entries=F('entries'),
).aggregate(Sum('entries'))
```

[`filter()`](#django.db.models.query.QuerySet.filter) and [`order_by()`](#django.db.models.query.QuerySet.order_by) can take expressions directly, but
expression construction and usage often does not happen in the same place (for
example, `QuerySet` method creates expressions, for later use in views).
`alias()` allows building complex expressions incrementally, possibly
spanning multiple methods and modules, refer to the expression parts by their
aliases and only use [`annotate()`](#django.db.models.query.QuerySet.annotate) for the final result.

#### `order_by()`

#### `order_by(*fields)`

By default, results returned by a `QuerySet` are ordered by the ordering
tuple given by the `ordering` option in the model's `Meta`. You can
override this on a per-`QuerySet` basis by using the `order_by` method.

実装例:

```
Entry.objects.filter(pub_date__year=2005).order_by('-pub_date', 'headline')
```

The result above will be ordered by `pub_date` descending, then by
`headline` ascending. The negative sign in front of `"-pub_date"` indicates
*descending* order. Ascending order is implied. To order randomly, use `"?"`,
like so:

```
Entry.objects.order_by('?')
```

Note: `order_by('?')` queries may be expensive and slow, depending on the
database backend you're using.

To order by a field in a different model, use the same syntax as when you are
querying across model relations. That is, the name of the field, followed by a
double underscore (`__`), followed by the name of the field in the new model,
and so on for as many models as you want to join. For example:

```
Entry.objects.order_by('blog__name', 'headline')
```

If you try to order by a field that is a relation to another model, Django will
use the default ordering on the related model, or order by the related model's
primary key if there is no [`Meta.ordering`](/ja/3.2/ref/models/options/#django.db.models.Options.ordering) specified. For example, since the `Blog`
model has no default ordering specified:

```
Entry.objects.order_by('blog')
```

...is identical to:

```
Entry.objects.order_by('blog__id')
```

If `Blog` had `ordering = ['name']`, then the first queryset would be
identical to:

```
Entry.objects.order_by('blog__name')
```

You can also order by [query expressions](/ja/3.2/ref/models/expressions/) by
calling [`asc()`](/ja/3.2/ref/models/expressions/#django.db.models.Expression.asc) or [`desc()`](/ja/3.2/ref/models/expressions/#django.db.models.Expression.desc) on the
expression:

```
Entry.objects.order_by(Coalesce('summary', 'headline').desc())
```

[`asc()`](/ja/3.2/ref/models/expressions/#django.db.models.Expression.asc) and [`desc()`](/ja/3.2/ref/models/expressions/#django.db.models.Expression.desc) have arguments
(`nulls_first` and `nulls_last`) that control how null values are sorted.

Be cautious when ordering by fields in related models if you are also using
[`distinct()`](#django.db.models.query.QuerySet.distinct). See the note in [`distinct()`](#django.db.models.query.QuerySet.distinct) for an explanation of how
related model ordering can change the expected results.

> **Note**
>
> It is permissible to specify a multi-valued field to order the results by
> (for example, a [`ManyToManyField`](/ja/3.2/ref/models/fields/#django.db.models.ManyToManyField) field, or the
> reverse relation of a [`ForeignKey`](/ja/3.2/ref/models/fields/#django.db.models.ForeignKey) field).
>
> Consider this case:
>
> ```
> class Event(Model):
>    parent = models.ForeignKey(
>        'self',
>        on_delete=models.CASCADE,
>        related_name='children',
>    )
>    date = models.DateField()
>
> Event.objects.order_by('children__date')
> ```
>
> Here, there could potentially be multiple ordering data for each `Event`;
> each `Event` with multiple `children` will be returned multiple times
> into the new `QuerySet` that `order_by()` creates. In other words,
> using `order_by()` on the `QuerySet` could return more items than you
> were working on to begin with - which is probably neither expected nor
> useful.
>
> Thus, take care when using multi-valued field to order the results. **If**
> you can be sure that there will only be one ordering piece of data for each
> of the items you're ordering, this approach should not present problems. If
> not, make sure the results are what you expect.

There's no way to specify whether ordering should be case sensitive. With
respect to case-sensitivity, Django will order results however your database
backend normally orders them.

You can order by a field converted to lowercase with
[`Lower`](/ja/3.2/ref/models/database-functions/#django.db.models.functions.Lower) which will achieve case-consistent
ordering:

```
Entry.objects.order_by(Lower('headline').desc())
```

If you don't want any ordering to be applied to a query, not even the default
ordering, call [`order_by()`](#django.db.models.query.QuerySet.order_by) with no parameters.

You can tell if a query is ordered or not by checking the
[`QuerySet.ordered`](#django.db.models.query.QuerySet.ordered) attribute, which will be `True` if the
`QuerySet` has been ordered in any way.

Each `order_by()` call will clear any previous ordering. For example, this
query will be ordered by `pub_date` and not `headline`:

```
Entry.objects.order_by('headline').order_by('pub_date')
```

> **Warning**
>
> Ordering is not a free operation. Each field you add to the ordering
> incurs a cost to your database. Each foreign key you add will
> implicitly include all of its default orderings as well.
>
> If a query doesn't have an ordering specified, results are returned from
> the database in an unspecified order. A particular ordering is guaranteed
> only when ordering by a set of fields that uniquely identify each object in
> the results. For example, if a `name` field isn't unique, ordering by it
> won't guarantee objects with the same name always appear in the same order.

#### `reverse()`

#### `reverse()`

Use the `reverse()` method to reverse the order in which a queryset's
elements are returned. Calling `reverse()` a second time restores the
ordering back to the normal direction.

To retrieve the "last" five items in a queryset, you could do this:

```
my_queryset.reverse()[:5]
```

Note that this is not quite the same as slicing from the end of a sequence in
Python. The above example will return the last item first, then the
penultimate item and so on. If we had a Python sequence and looked at
`seq[-5:]`, we would see the fifth-last item first. Django doesn't support
that mode of access (slicing from the end), because it's not possible to do it
efficiently in SQL.

Also, note that `reverse()` should generally only be called on a `QuerySet`
which has a defined ordering (e.g., when querying against a model which defines
a default ordering, or when using [`order_by()`](#django.db.models.query.QuerySet.order_by)). If no such ordering is
defined for a given `QuerySet`, calling `reverse()` on it has no real
effect (the ordering was undefined prior to calling `reverse()`, and will
remain undefined afterward).

#### `distinct()`

#### `distinct(*fields)`

Returns a new `QuerySet` that uses `SELECT DISTINCT` in its SQL query. This
eliminates duplicate rows from the query results.

By default, a `QuerySet` will not eliminate duplicate rows. In practice, this
is rarely a problem, because simple queries such as `Blog.objects.all()`
don't introduce the possibility of duplicate result rows. However, if your
query spans multiple tables, it's possible to get duplicate results when a
`QuerySet` is evaluated. That's when you'd use `distinct()`.

> **Note**
>
> Any fields used in an [`order_by()`](#django.db.models.query.QuerySet.order_by) call are included in the SQL
> `SELECT` columns. This can sometimes lead to unexpected results when used
> in conjunction with `distinct()`. If you order by fields from a related
> model, those fields will be added to the selected columns and they may make
> otherwise duplicate rows appear to be distinct. Since the extra columns
> don't appear in the returned results (they are only there to support
> ordering), it sometimes looks like non-distinct results are being returned.
>
> Similarly, if you use a [`values()`](#django.db.models.query.QuerySet.values) query to restrict the columns
> selected, the columns used in any [`order_by()`](#django.db.models.query.QuerySet.order_by) (or default model
> ordering) will still be involved and may affect uniqueness of the results.
>
> The moral here is that if you are using `distinct()` be careful about
> ordering by related models. Similarly, when using `distinct()` and
> [`values()`](#django.db.models.query.QuerySet.values) together, be careful when ordering by fields not in the
> [`values()`](#django.db.models.query.QuerySet.values) call.

On PostgreSQL only, you can pass positional arguments (`*fields`) in order to
specify the names of fields to which the `DISTINCT` should apply. This
translates to a `SELECT DISTINCT ON` SQL query. Here's the difference. For a
normal `distinct()` call, the database compares *each* field in each row when
determining which rows are distinct. For a `distinct()` call with specified
field names, the database will only compare the specified field names.

> **Note**
>
> When you specify field names, you *must* provide an `order_by()` in the
> `QuerySet`, and the fields in `order_by()` must start with the fields in
> `distinct()`, in the same order.
>
> For example, `SELECT DISTINCT ON (a)` gives you the first row for each
> value in column `a`. If you don't specify an order, you'll get some
> arbitrary row.

Examples (those after the first will only work on PostgreSQL):

```
>>> Author.objects.distinct()
[...]

>>> Entry.objects.order_by('pub_date').distinct('pub_date')
[...]

>>> Entry.objects.order_by('blog').distinct('blog')
[...]

>>> Entry.objects.order_by('author', 'pub_date').distinct('author', 'pub_date')
[...]

>>> Entry.objects.order_by('blog__name', 'mod_date').distinct('blog__name', 'mod_date')
[...]

>>> Entry.objects.order_by('author', 'pub_date').distinct('author')
[...]
```

> **Note**
>
> Keep in mind that [`order_by()`](#django.db.models.query.QuerySet.order_by) uses any default related model ordering
> that has been defined. You might have to explicitly order by the relation
> `_id` or referenced field to make sure the `DISTINCT ON` expressions
> match those at the beginning of the `ORDER BY` clause. For example, if
> the `Blog` model defined an [`ordering`](/ja/3.2/ref/models/options/#django.db.models.Options.ordering) by
> `name`:
>
> ```
> Entry.objects.order_by('blog').distinct('blog')
> ```
>
> ...wouldn't work because the query would be ordered by `blog__name` thus
> mismatching the `DISTINCT ON` expression. You'd have to explicitly order
> by the relation `_id` field (`blog_id` in this case) or the referenced
> one (`blog__pk`) to make sure both expressions match.

#### `values()`

#### `values(*fields, **expressions)`

Returns a `QuerySet` that returns dictionaries, rather than model instances,
when used as an iterable.

Each of those dictionaries represents an object, with the keys corresponding to
the attribute names of model objects.

This example compares the dictionaries of `values()` with the normal model
objects:

```
# This list contains a Blog object.
>>> Blog.objects.filter(name__startswith='Beatles')
<QuerySet [<Blog: Beatles Blog>]>

# This list contains a dictionary.
>>> Blog.objects.filter(name__startswith='Beatles').values()
<QuerySet [{'id': 1, 'name': 'Beatles Blog', 'tagline': 'All the latest Beatles news.'}]>
```

The `values()` method takes optional positional arguments, `*fields`, which
specify field names to which the `SELECT` should be limited. If you specify
the fields, each dictionary will contain only the field keys/values for the
fields you specify. If you don't specify the fields, each dictionary will
contain a key and value for every field in the database table.

実装例:

```
>>> Blog.objects.values()
<QuerySet [{'id': 1, 'name': 'Beatles Blog', 'tagline': 'All the latest Beatles news.'}]>
>>> Blog.objects.values('id', 'name')
<QuerySet [{'id': 1, 'name': 'Beatles Blog'}]>
```

The `values()` method also takes optional keyword arguments,
`**expressions`, which are passed through to [`annotate()`](#django.db.models.query.QuerySet.annotate):

```
>>> from django.db.models.functions import Lower
>>> Blog.objects.values(lower_name=Lower('name'))
<QuerySet [{'lower_name': 'beatles blog'}]>
```

You can use built-in and [custom lookups](/ja/3.2/howto/custom-lookups/) in
ordering. For example:

```
>>> from django.db.models import CharField
>>> from django.db.models.functions import Lower
>>> CharField.register_lookup(Lower)
>>> Blog.objects.values('name__lower')
<QuerySet [{'name__lower': 'beatles blog'}]>
```

An aggregate within a `values()` clause is applied before other arguments
within the same `values()` clause. If you need to group by another value,
add it to an earlier `values()` clause instead. For example:

```
>>> from django.db.models import Count
>>> Blog.objects.values('entry__authors', entries=Count('entry'))
<QuerySet [{'entry__authors': 1, 'entries': 20}, {'entry__authors': 1, 'entries': 13}]>
>>> Blog.objects.values('entry__authors').annotate(entries=Count('entry'))
<QuerySet [{'entry__authors': 1, 'entries': 33}]>
```

A few subtleties that are worth mentioning:

- If you have a field called `foo` that is a
  [`ForeignKey`](/ja/3.2/ref/models/fields/#django.db.models.ForeignKey), the default `values()` call
  will return a dictionary key called `foo_id`, since this is the name
  of the hidden model attribute that stores the actual value (the `foo`
  attribute refers to the related model). When you are calling
  `values()` and passing in field names, you can pass in either `foo`
  or `foo_id` and you will get back the same thing (the dictionary key
  will match the field name you passed in).

  例:

  ```
  >>> Entry.objects.values()
  <QuerySet [{'blog_id': 1, 'headline': 'First Entry', ...}, ...]>

  >>> Entry.objects.values('blog')
  <QuerySet [{'blog': 1}, ...]>

  >>> Entry.objects.values('blog_id')
  <QuerySet [{'blog_id': 1}, ...]>
  ```
- When using `values()` together with [`distinct()`](#django.db.models.query.QuerySet.distinct), be aware that
  ordering can affect the results. See the note in [`distinct()`](#django.db.models.query.QuerySet.distinct) for
  details.
- If you use a `values()` clause after an [`extra()`](#django.db.models.query.QuerySet.extra) call,
  any fields defined by a `select` argument in the [`extra()`](#django.db.models.query.QuerySet.extra) must
  be explicitly included in the `values()` call. Any [`extra()`](#django.db.models.query.QuerySet.extra) call
  made after a `values()` call will have its extra selected fields
  ignored.
- Calling [`only()`](#django.db.models.query.QuerySet.only) and [`defer()`](#django.db.models.query.QuerySet.defer) after `values()` doesn't make
  sense, so doing so will raise a `TypeError`.
- Combining transforms and aggregates requires the use of two [`annotate()`](#django.db.models.query.QuerySet.annotate)
  calls, either explicitly or as keyword arguments to [`values()`](#django.db.models.query.QuerySet.values). As above,
  if the transform has been registered on the relevant field type the first
  [`annotate()`](#django.db.models.query.QuerySet.annotate) can be omitted, thus the following examples are equivalent:

  ```
  >>> from django.db.models import CharField, Count
  >>> from django.db.models.functions import Lower
  >>> CharField.register_lookup(Lower)
  >>> Blog.objects.values('entry__authors__name__lower').annotate(entries=Count('entry'))
  <QuerySet [{'entry__authors__name__lower': 'test author', 'entries': 33}]>
  >>> Blog.objects.values(
  ...     entry__authors__name__lower=Lower('entry__authors__name')
  ... ).annotate(entries=Count('entry'))
  <QuerySet [{'entry__authors__name__lower': 'test author', 'entries': 33}]>
  >>> Blog.objects.annotate(
  ...     entry__authors__name__lower=Lower('entry__authors__name')
  ... ).values('entry__authors__name__lower').annotate(entries=Count('entry'))
  <QuerySet [{'entry__authors__name__lower': 'test author', 'entries': 33}]>
  ```

It is useful when you know you're only going to need values from a small number
of the available fields and you won't need the functionality of a model
instance object. It's more efficient to select only the fields you need to use.

Finally, note that you can call `filter()`, `order_by()`, etc. after the
`values()` call, that means that these two calls are identical:

```
Blog.objects.values().order_by('id')
Blog.objects.order_by('id').values()
```

The people who made Django prefer to put all the SQL-affecting methods first,
followed (optionally) by any output-affecting methods (such as `values()`),
but it doesn't really matter. This is your chance to really flaunt your
individualism.

You can also refer to fields on related models with reverse relations through
`OneToOneField`, `ForeignKey` and `ManyToManyField` attributes:

```
>>> Blog.objects.values('name', 'entry__headline')
<QuerySet [{'name': 'My blog', 'entry__headline': 'An entry'},
     {'name': 'My blog', 'entry__headline': 'Another entry'}, ...]>
```

> **Warning**
>
> Because [`ManyToManyField`](/ja/3.2/ref/models/fields/#django.db.models.ManyToManyField) attributes and reverse
> relations can have multiple related rows, including these can have a
> multiplier effect on the size of your result set. This will be especially
> pronounced if you include multiple such fields in your `values()` query,
> in which case all possible combinations will be returned.

> **Boolean values for JSONField on SQLite**
>
> Due to the way the `JSON_EXTRACT` SQL function is implemented on SQLite,
> `values()` will return `1` and `0` instead of `True` and `False`
> for [`JSONField`](/ja/3.2/ref/models/fields/#django.db.models.JSONField) key transforms.

#### `values_list()`

#### `values_list(*fields, flat=False, named=False)`

This is similar to `values()` except that instead of returning dictionaries,
it returns tuples when iterated over. Each tuple contains the value from the
respective field or expression passed into the `values_list()` call — so the
first item is the first field, etc. For example:

```
>>> Entry.objects.values_list('id', 'headline')
<QuerySet [(1, 'First entry'), ...]>
>>> from django.db.models.functions import Lower
>>> Entry.objects.values_list('id', Lower('headline'))
<QuerySet [(1, 'first entry'), ...]>
```

If you only pass in a single field, you can also pass in the `flat`
parameter. If `True`, this will mean the returned results are single values,
rather than one-tuples. An example should make the difference clearer:

```
>>> Entry.objects.values_list('id').order_by('id')
<QuerySet[(1,), (2,), (3,), ...]>

>>> Entry.objects.values_list('id', flat=True).order_by('id')
<QuerySet [1, 2, 3, ...]>
```

It is an error to pass in `flat` when there is more than one field.

You can pass `named=True` to get results as a
[`namedtuple()`](https://docs.python.org/3/library/collections.html#collections.namedtuple):

```
>>> Entry.objects.values_list('id', 'headline', named=True)
<QuerySet [Row(id=1, headline='First entry'), ...]>
```

Using a named tuple may make use of the results more readable, at the expense
of a small performance penalty for transforming the results into a named tuple.

If you don't pass any values to `values_list()`, it will return all the
fields in the model, in the order they were declared.

A common need is to get a specific field value of a certain model instance. To
achieve that, use `values_list()` followed by a `get()` call:

```
>>> Entry.objects.values_list('headline', flat=True).get(pk=1)
'First entry'
```

`values()` and `values_list()` are both intended as optimizations for a
specific use case: retrieving a subset of data without the overhead of creating
a model instance. This metaphor falls apart when dealing with many-to-many and
other multivalued relations (such as the one-to-many relation of a reverse
foreign key) because the "one row, one object" assumption doesn't hold.

For example, notice the behavior when querying across a
[`ManyToManyField`](/ja/3.2/ref/models/fields/#django.db.models.ManyToManyField):

```
>>> Author.objects.values_list('name', 'entry__headline')
<QuerySet [('Noam Chomsky', 'Impressions of Gaza'),
 ('George Orwell', 'Why Socialists Do Not Believe in Fun'),
 ('George Orwell', 'In Defence of English Cooking'),
 ('Don Quixote', None)]>
```

Authors with multiple entries appear multiple times and authors without any
entries have `None` for the entry headline.

Similarly, when querying a reverse foreign key, `None` appears for entries
not having any author:

```
>>> Entry.objects.values_list('authors')
<QuerySet [('Noam Chomsky',), ('George Orwell',), (None,)]>
```

> **Boolean values for JSONField on SQLite**
>
> Due to the way the `JSON_EXTRACT` SQL function is implemented on SQLite,
> `values_list()` will return `1` and `0` instead of `True` and
> `False` for [`JSONField`](/ja/3.2/ref/models/fields/#django.db.models.JSONField) key transforms.

#### `dates()`

#### `dates(field, kind, order='ASC')`

Returns a `QuerySet` that evaluates to a list of [`datetime.date`](https://docs.python.org/3/library/datetime.html#datetime.date)
objects representing all available dates of a particular kind within the
contents of the `QuerySet`.

`field` should be the name of a `DateField` of your model.
`kind` should be either `"year"`, `"month"`, `"week"`, or `"day"`.
Each [`datetime.date`](https://docs.python.org/3/library/datetime.html#datetime.date) object in the result list is "truncated" to the
given `type`.

- `"year"` returns a list of all distinct year values for the field.
- `"month"` returns a list of all distinct year/month values for the
  field.
- `"week"` returns a list of all distinct year/week values for the field. All
  dates will be a Monday.
- `"day"` returns a list of all distinct year/month/day values for the
  field.

`order`, which defaults to `'ASC'`, should be either `'ASC'` or
`'DESC'`. This specifies how to order the results.

例:

```
>>> Entry.objects.dates('pub_date', 'year')
[datetime.date(2005, 1, 1)]
>>> Entry.objects.dates('pub_date', 'month')
[datetime.date(2005, 2, 1), datetime.date(2005, 3, 1)]
>>> Entry.objects.dates('pub_date', 'week')
[datetime.date(2005, 2, 14), datetime.date(2005, 3, 14)]
>>> Entry.objects.dates('pub_date', 'day')
[datetime.date(2005, 2, 20), datetime.date(2005, 3, 20)]
>>> Entry.objects.dates('pub_date', 'day', order='DESC')
[datetime.date(2005, 3, 20), datetime.date(2005, 2, 20)]
>>> Entry.objects.filter(headline__contains='Lennon').dates('pub_date', 'day')
[datetime.date(2005, 3, 20)]
```

#### `datetimes()`

#### `datetimes(field_name, kind, order='ASC', tzinfo=None, is_dst=None)`

Returns a `QuerySet` that evaluates to a list of [`datetime.datetime`](https://docs.python.org/3/library/datetime.html#datetime.datetime)
objects representing all available dates of a particular kind within the
contents of the `QuerySet`.

`field_name` should be the name of a `DateTimeField` of your model.

`kind` should be either `"year"`, `"month"`, `"week"`, `"day"`,
`"hour"`, `"minute"`, or `"second"`. Each [`datetime.datetime`](https://docs.python.org/3/library/datetime.html#datetime.datetime)
object in the result list is "truncated" to the given `type`.

`order`, which defaults to `'ASC'`, should be either `'ASC'` or
`'DESC'`. This specifies how to order the results.

`tzinfo` defines the time zone to which datetimes are converted prior to
truncation. Indeed, a given datetime has different representations depending
on the time zone in use. This parameter must be a [`datetime.tzinfo`](https://docs.python.org/3/library/datetime.html#datetime.tzinfo)
object. If it's `None`, Django uses the [current time zone](/ja/3.2/topics/i18n/timezones/#default-current-time-zone). It has no effect when [`USE_TZ`](/ja/3.2/ref/settings/#std-setting-USE_TZ) is
`False`.

`is_dst` indicates whether or not `pytz` should interpret nonexistent and
ambiguous datetimes in daylight saving time. By default (when `is_dst=None`),
`pytz` raises an exception for such datetimes.

> **New in Django 3.1**
>
> The `is_dst` parameter was added.

> **Note**
>
> This function performs time zone conversions directly in the database.
> As a consequence, your database must be able to interpret the value of
> `tzinfo.tzname(None)`. This translates into the following requirements:
>
> - SQLite: no requirements. Conversions are performed in Python with [pytz](http://pytz.sourceforge.net/)
>   (installed when you install Django).
> - PostgreSQL: no requirements (see [Time Zones](https://www.postgresql.org/docs/current/datatype-datetime.html#DATATYPE-TIMEZONES)).
> - Oracle: no requirements (see [Choosing a Time Zone File](https://docs.oracle.com/en/database/oracle/oracle-database/18/nlspg/datetime-data-types-and-time-zone-support.html#GUID-805AB986-DE12-4FEA-AF56-5AABCD2132DF)).
> - MySQL: load the time zone tables with [mysql\_tzinfo\_to\_sql](https://dev.mysql.com/doc/refman/en/mysql-tzinfo-to-sql.html).

#### `none()`

#### `none()`

Calling `none()` will create a queryset that never returns any objects and no
query will be executed when accessing the results. A `qs.none()` queryset
is an instance of `EmptyQuerySet`.

例:

```
>>> Entry.objects.none()
<QuerySet []>
>>> from django.db.models.query import EmptyQuerySet
>>> isinstance(Entry.objects.none(), EmptyQuerySet)
True
```

#### `all()`

#### `all()`

Returns a *copy* of the current `QuerySet` (or `QuerySet` subclass).  This
can be useful in situations where you might want to pass in either a model
manager or a `QuerySet` and do further filtering on the result. After calling
`all()` on either object, you'll definitely have a `QuerySet` to work with.

When a `QuerySet` is [evaluated](#when-querysets-are-evaluated), it
typically caches its results. If the data in the database might have changed
since a `QuerySet` was evaluated, you can get updated results for the same
query by calling `all()` on a previously evaluated `QuerySet`.

#### `union()`

#### `union(*other_qs, all=False)`

Uses SQL's `UNION` operator to combine the results of two or more
`QuerySet`s. For example:

```
>>> qs1.union(qs2, qs3)
```

The `UNION` operator selects only distinct values by default. To allow
duplicate values, use the `all=True` argument.

`union()`, `intersection()`, and `difference()` return model instances
of the type of the first `QuerySet` even if the arguments are `QuerySet`s
of other models. Passing different models works as long as the `SELECT` list
is the same in all `QuerySet`s (at least the types, the names don't matter
as long as the types are in the same order). In such cases, you must use the
column names from the first `QuerySet` in `QuerySet` methods applied to the
resulting `QuerySet`. For example:

```
>>> qs1 = Author.objects.values_list('name')
>>> qs2 = Entry.objects.values_list('headline')
>>> qs1.union(qs2).order_by('name')
```

In addition, only `LIMIT`, `OFFSET`, `COUNT(*)`, `ORDER BY`, and
specifying columns (i.e. slicing, [`count()`](#django.db.models.query.QuerySet.count), [`exists()`](#django.db.models.query.QuerySet.exists),
[`order_by()`](#django.db.models.query.QuerySet.order_by), and [`values()`](#django.db.models.query.QuerySet.values)/[`values_list()`](#django.db.models.query.QuerySet.values_list)) are allowed
on the resulting `QuerySet`. Further, databases place restrictions on
what operations are allowed in the combined queries. For example, most
databases don't allow `LIMIT` or `OFFSET` in the combined queries.

#### `intersection()`

#### `intersection(*other_qs)`

Uses SQL's `INTERSECT` operator to return the shared elements of two or more
`QuerySet`s. For example:

```
>>> qs1.intersection(qs2, qs3)
```

See [`union()`](#django.db.models.query.QuerySet.union) for some restrictions.

#### `difference()`

#### `difference(*other_qs)`

Uses SQL's `EXCEPT` operator to keep only elements present in the
`QuerySet` but not in some other `QuerySet`s. For example:

```
>>> qs1.difference(qs2, qs3)
```

See [`union()`](#django.db.models.query.QuerySet.union) for some restrictions.

#### `select_related()`

#### `select_related(*fields)`

Returns a `QuerySet` that will "follow" foreign-key relationships, selecting
additional related-object data when it executes its query. This is a
performance booster which results in a single more complex query but means
later use of foreign-key relationships won't require database queries.

The following examples illustrate the difference between plain lookups and
`select_related()` lookups. Here's standard lookup:

```
# Hits the database.
e = Entry.objects.get(id=5)

# Hits the database again to get the related Blog object.
b = e.blog
```

And here's `select_related` lookup:

```
# Hits the database.
e = Entry.objects.select_related('blog').get(id=5)

# Doesn't hit the database, because e.blog has been prepopulated
# in the previous query.
b = e.blog
```

You can use `select_related()` with any queryset of objects:

```
from django.utils import timezone

# Find all the blogs with entries scheduled to be published in the future.
blogs = set()

for e in Entry.objects.filter(pub_date__gt=timezone.now()).select_related('blog'):
    # Without select_related(), this would make a database query for each
    # loop iteration in order to fetch the related blog for each entry.
    blogs.add(e.blog)
```

The order of `filter()` and `select_related()` chaining isn't important.
These querysets are equivalent:

```
Entry.objects.filter(pub_date__gt=timezone.now()).select_related('blog')
Entry.objects.select_related('blog').filter(pub_date__gt=timezone.now())
```

You can follow foreign keys in a similar way to querying them. If you have the
following models:

```
from django.db import models

class City(models.Model):
    # ...
    pass

class Person(models.Model):
    # ...
    hometown = models.ForeignKey(
        City,
        on_delete=models.SET_NULL,
        blank=True,
        null=True,
    )

class Book(models.Model):
    # ...
    author = models.ForeignKey(Person, on_delete=models.CASCADE)
```

... then a call to `Book.objects.select_related('author__hometown').get(id=4)`
will cache the related `Person` *and* the related `City`:

```
# Hits the database with joins to the author and hometown tables.
b = Book.objects.select_related('author__hometown').get(id=4)
p = b.author         # Doesn't hit the database.
c = p.hometown       # Doesn't hit the database.

# Without select_related()...
b = Book.objects.get(id=4)  # Hits the database.
p = b.author         # Hits the database.
c = p.hometown       # Hits the database.
```

You can refer to any [`ForeignKey`](/ja/3.2/ref/models/fields/#django.db.models.ForeignKey) or
[`OneToOneField`](/ja/3.2/ref/models/fields/#django.db.models.OneToOneField) relation in the list of fields
passed to `select_related()`.

You can also refer to the reverse direction of a
[`OneToOneField`](/ja/3.2/ref/models/fields/#django.db.models.OneToOneField) in the list of fields passed to
`select_related` — that is, you can traverse a
[`OneToOneField`](/ja/3.2/ref/models/fields/#django.db.models.OneToOneField) back to the object on which the field
is defined. Instead of specifying the field name, use the [`related_name`](/ja/3.2/ref/models/fields/#django.db.models.ForeignKey.related_name) for the field on the related object.

There may be some situations where you wish to call `select_related()` with a
lot of related objects, or where you don't know all of the relations. In these
cases it is possible to call `select_related()` with no arguments. This will
follow all non-null foreign keys it can find - nullable foreign keys must be
specified. This is not recommended in most cases as it is likely to make the
underlying query more complex, and return more data, than is actually needed.

If you need to clear the list of related fields added by past calls of
`select_related` on a `QuerySet`, you can pass `None` as a parameter:

```
>>> without_relations = queryset.select_related(None)
```

Chaining `select_related` calls works in a similar way to other methods -
that is that `select_related('foo', 'bar')` is equivalent to
`select_related('foo').select_related('bar')`.

#### `prefetch_related()`

#### `prefetch_related(*lookups)`

`QuerySet` を返し、単一のバッチで、指定した検索それぞれに対して関連するオブジェクトを自動的に取り出します。

目的は `select_related` と似ていて、関連オブジェクトにアクセスすることで多量のデータベースクエリが発生してしまうのを防ぐために作られていますが、その手段が異なります。

`select_related` は、SQL の結合を生成して `SELECT` 句に関連オブジェクトを含むことでクエリ発行を抑制します。このため、`select_related` は同一のデータベースクエリ内で関連オブジェクトを取得します。しかしながら、'多量の' リレーションシップを結合することで非常に大きな結果になってしまうのを防ぐため、`select_related` は単一値のリレーションシップ - つまり外部キーと一対一 - のみに制限されています。

`prefetch_related` はこれとは異なり、各リレーションシップに対して別々に検索を行い、Python で '結合’ を行います。これにより、`select_related` で可能な外部キーおよび一対一のリレーションシップだけでなく、多対多および多対一のオブジェクトも事前に読み込んでおけるようになります。また、[`GenericRelation`](/ja/3.2/ref/contrib/contenttypes/#django.contrib.contenttypes.fields.GenericRelation) や [`GenericForeignKey`](/ja/3.2/ref/contrib/contenttypes/#django.contrib.contenttypes.fields.GenericForeignKey) の事前読み込みも可能となりますが、使用できるのは結果が同種の場合に限ります。例えば、`GenericForeignKey` が参照するオブジェクトの事前読み込みは、クエリが 1 つの `ContentType` に制限されている場合のみ可能です。

例えば、以下のようなモデルがあると考えます:

```
from django.db import models

class Topping(models.Model):
    name = models.CharField(max_length=30)

class Pizza(models.Model):
    name = models.CharField(max_length=50)
    toppings = models.ManyToManyField(Topping)

    def __str__(self):
        return "%s (%s)" % (
            self.name,
            ", ".join(topping.name for topping in self.toppings.all()),
        )
```

そして以下を実行します:

```
>>> Pizza.objects.all()
["Hawaiian (ham, pineapple)", "Seafood (prawns, smoked salmon)"...
```

上記の問題点は、`Pizza.__str__()` が `self.toppings.all()` を要求するたびデータベースにクエリを発行する必要があるので、`Pizza.objects.all()` は Toppings テーブルで **すべての** Pizza `QuerySet` アイテムに対してクエリを実行することです。

`prefetch_related` を使えばわすか 2 つのクエリに削減することができます:

```
>>> Pizza.objects.all().prefetch_related('toppings')
```

上記は、それぞれの `Pizza` に対する `self.toppings.all()` を暗示しています; `self.toppings.all()` を呼び出すとき、アイテム取得のためいちいちデータベースに行かず、単一クエリ内で事前読み込みされた `QuerySet` キャッシュを利用します。

つまり、すべての関連するトッピングが単一のクエリで取り出され、関連する結果の事前読み込みキャッシュを持つ `QuerySet` の生成に使われます。この `QuerySet` は `self.toppings.all()` の呼び出しで使用されます。

`prefetch_related()` 内の追加クエリは `QuerySet` の評価が開始され、主クエリが実行されたときに実行されます。

モデルインスタンスのイテラブルを保持する場合は、 [`prefetch_related_objects()`](#django.db.models.prefetch_related_objects) 関数を使ってこれらインスタンスに関連する属性を事前読み込みしておくことができます。

主 `QuerySet` の結果のキャッシュと指定されたすべての関連オブジェクトは、メモリ上に完全にロードされます。これにより、クエリがデータベースで実行された後であっても必要になるまでオブジェクトをメモリに呼び出さないという一般的な `QuerySet` の動作が変わる点に注意してください。

> **Note**
>
> いつもの `QuerySet` と同じく、異なるデータベースクエリを意味するチェーンメソッドは、読み込み済みのキャッシュを無視して新たなデータベースクエリを発行します。従って、以下のように書いた場合:
>
> ```
> >>> pizzas = Pizza.objects.prefetch_related('toppings')
> >>> [list(pizza.toppings.filter(spicy=True)) for pizza in pizzas]
> ```
>
> ... `pizza.toppings.all()` が事前読み込みされたことは役に立たないことになります。`prefetch_related('toppings')` は `pizza.toppings.all()` を意味しますが、`pizza.toppings.filter()` は新しくまた異なるクエリなのです。事前読み込みキャッシュはここでは役立ちません; 実際は不要なデータベースクエリを発行することでパフォーマンス低下の原因にさえなります。なので、この機能は注意深く使用してください！
>
> また、[`related managers`](/ja/3.2/ref/models/relations/#django.db.models.fields.related.RelatedManager) で [`add()`](/ja/3.2/ref/models/relations/#django.db.models.fields.related.RelatedManager.add)、[`remove()`](/ja/3.2/ref/models/relations/#django.db.models.fields.related.RelatedManager.remove)、[`clear()`](/ja/3.2/ref/models/relations/#django.db.models.fields.related.RelatedManager.clear)、[`set()`](/ja/3.2/ref/models/relations/#django.db.models.fields.related.RelatedManager.set) といったデータベース互換のメソッドを呼び出した場合、リレーション用の事前呼び出しキャッシュはすべてクリアされます。

通常の結合構文を使って、関連フィールドの関連フィールドを行うこともできます。上記の例二以下のモデルを追加したと考えましょう:

```
class Restaurant(models.Model):
    pizzas = models.ManyToManyField(Pizza, related_name='restaurants')
    best_pizza = models.ForeignKey(Pizza, related_name='championed_by', on_delete=models.CASCADE)
```

以下は適切です:

```
>>> Restaurant.objects.prefetch_related('pizzas__toppings')
```

これはレストランに所属するすべてのピザと、これらのピザに所属するすべてのトッピングを事前読み込みします。これにより、3 つのデータベースクエリが発行されます - 一つはレストランのため、一つはピザのため、そしてもう一つはトッピングのためです。

```
>>> Restaurant.objects.prefetch_related('best_pizza__toppings')
```

これは、ベストなピザと、各レストランに対するベストなピザに対するすべてのトッピングを取得します。これは 3 つのデータベースクエリで実行されます - 一つはレストランのため、一つは 'ベストなピザ' のため、おっして一つはトッピングのためです。

The `best_pizza` relationship could also be fetched using `select_related`
to reduce the query count to 2:

```
>>> Restaurant.objects.select_related('best_pizza').prefetch_related('best_pizza__toppings')
```

事前読み込みは主クエリ (```select_related``が必要とする結合を含みます) の後に実行されるので ``best_pizza``` がすでに読み込み済みかを検査することが可能で、再度読み込まないよう処理を飛ばします。

`prefetch_related` をチェーンで呼び出すと、事前読み込みの検索を蓄積します。すべての `prefetch_related` 動作をクリアするには `None` パラメータを渡してください。

```
>>> non_prefetched = qs.prefetch_related(None)
```

`prefetch_related` を使う際の違いの一つは、一つのクエリで生成されたオブジェクトは関連した異なるオブジェクト間で共有できることです。例えば、単一の Python モデルインスタンスは、返されたオブジェクトのツリー内で複数現れることができます。これは通常、外部キーで発生します。通常、この動作は問題とはならず、むしろメモリと CPU の両方を節約します。

`prefetch_related` は `GenericForeignKey` リレーションシップの事前読み込みをサポートしますが、クエリ数はデータに依存します。`GenericForeignKey` は複数のテーブルでデータを参照できるので、すべてのアイテムに対する単一のクエリではなく各テーブルを参照するクエリが必要となります。関連する行がまだ取得されていない場合、`ContentType` テーブルで追加的なクエリが必要となります。

多くの場合 `prefetch_related` は 'IN' オペラータを使用する単一の SQL クエリを意味します。これは、大きな `QuerySet` に対して大きな 'IN' 節が生成され、データベースによってはSQL クエリの解析および実行の際にパフォーマンスの問題の原因となり得ます。利用状況に合わせて使用するよう心がけてください！

クエリを実行するために `iterator()` を使用している場合、`prefetch_related()` の呼び出しを同時に使うのは無意味なので無視されます。

[`Prefetch`](#django.db.models.Prefetch) オブジェクトを使って、事前呼び出しの操作をより細かくコントロールできます。

`Prefetch` の最もシンプルな形では、従来の文字列ベースの検索と同等になります:

```
>>> from django.db.models import Prefetch
>>> Restaurant.objects.prefetch_related(Prefetch('pizzas__toppings'))
```

オプションの `queryset` 引数を使って、独自のクエリセットを指定することができます。これは、クエリセットのデフォルトの並び順を変更するために使用できます:

```
>>> Restaurant.objects.prefetch_related(
...     Prefetch('pizzas__toppings', queryset=Toppings.objects.order_by('name')))
```

もしくは、さらにクエリ数を減らすために、可能なときは [`select_related()`](#django.db.models.query.QuerySet.select_related) を呼び出します:

```
>>> Pizza.objects.prefetch_related(
...     Prefetch('restaurants', queryset=Restaurant.objects.select_related('best_pizza')))
```

また、オプションの `to_attr` で、事前読み込みの結果を独自の属性に割り当てることもできます。結果はリストに直接格納されます。

これにより、異なる `QuerySet` で同じリレーションを複数回事前読み込みできるようになります; 例えば:

```
>>> vegetarian_pizzas = Pizza.objects.filter(vegetarian=True)
>>> Restaurant.objects.prefetch_related(
...     Prefetch('pizzas', to_attr='menu'),
...     Prefetch('pizzas', queryset=vegetarian_pizzas, to_attr='vegetarian_menu'))
```

独自の `to_attr` で生成した検索はいつも通り他の検索でも使用可能です:

```
>>> vegetarian_pizzas = Pizza.objects.filter(vegetarian=True)
>>> Restaurant.objects.prefetch_related(
...     Prefetch('pizzas', queryset=vegetarian_pizzas, to_attr='vegetarian_menu'),
...     'vegetarian_menu__toppings')
```

`to_attr` の使用は、関係するマネージャのキャッシュにフィルタした結果を格納するよりも明確になるよう、事前読み込みした結果をフィルタする際に推奨されます:

```
>>> queryset = Pizza.objects.filter(vegetarian=True)
>>>
>>> # Recommended:
>>> restaurants = Restaurant.objects.prefetch_related(
...     Prefetch('pizzas', queryset=queryset, to_attr='vegetarian_pizzas'))
>>> vegetarian_pizzas = restaurants[0].vegetarian_pizzas
>>>
>>> # Not recommended:
>>> restaurants = Restaurant.objects.prefetch_related(
...     Prefetch('pizzas', queryset=queryset))
>>> vegetarian_pizzas = restaurants[0].pizzas.all()
```

独自の事前読み込みは、`ForeignKey` や `OneToOneField` のような単一の関連リレーションでも機能します。一般的にはこういう場合 [`select_related()`](#django.db.models.query.QuerySet.select_related) を使いたくなるでしょうが、`QuerySet` が役立つ場面も多くあります:

- 関連モデルに対してさらに事前読み込みを行う `QuerySet` を使用したい。
- 関連オブジェクトのサブセットのみを事前読み込みしたい。
- You want to use performance optimization techniques like
  [`deferred fields`](#django.db.models.query.QuerySet.defer):

  ```
  >>> queryset = Pizza.objects.only('name')
  >>>
  >>> restaurants = Restaurant.objects.prefetch_related(
  ...     Prefetch('best_pizza', queryset=queryset))
  ```

When using multiple databases, `Prefetch` will respect your choice of
database. If the inner query does not specify a database, it will use the
database selected by the outer query. All of the following are valid:

```
>>> # Both inner and outer queries will use the 'replica' database
>>> Restaurant.objects.prefetch_related('pizzas__toppings').using('replica')
>>> Restaurant.objects.prefetch_related(
...     Prefetch('pizzas__toppings'),
... ).using('replica')
>>>
>>> # Inner will use the 'replica' database; outer will use 'default' database
>>> Restaurant.objects.prefetch_related(
...     Prefetch('pizzas__toppings', queryset=Toppings.objects.using('replica')),
... )
>>>
>>> # Inner will use 'replica' database; outer will use 'cold-storage' database
>>> Restaurant.objects.prefetch_related(
...     Prefetch('pizzas__toppings', queryset=Toppings.objects.using('replica')),
... ).using('cold-storage')
```

> **Note**
>
> 検索の順番は重要です。
>
> 以下の例を見てみましょう:
>
> ```
> >>> prefetch_related('pizzas__toppings', 'pizzas')
> ```
>
> `'pizzas__toppings'` がすでに必要な情報を含んでいるので、並び替えしていないにもかかわらずこれは機能します。したがって 2 番目の引数 `'pizzas'` は実際には不要です。
>
> ```
> >>> prefetch_related('pizzas__toppings', Prefetch('pizzas', queryset=Pizza.objects.all()))
> ```
>
> 事前に見た検索クエリを再定義しようとしているため、これは `ValueError` を発生させます。 `'pizzas'` を横切るために\`\`'pizzas\_\_toppings'\`\` の一部として暗黙的なクエリセットが生成される点に留意してください。
>
> ```
> >>> prefetch_related('pizza_list__toppings', Prefetch('pizzas', to_attr='pizza_list'))
> ```
>
> `'pizza_list__toppings'` が実行される際にまだ `'pizza_list'` が存在していないため、これは\`\`AttributeError\`\` を引き起こします。
>
> この考慮事項は、`Prefetch` の使用に限りません。高度なテクニックのいくつかでは、余計なクエリの生成を防止するため、特定の順序で検索を実施する必要があるかもしれません; したがって、`prefetch_related` の引数には常に注意を払うことを推奨します。

#### `extra()`

#### `extra(select=None, where=None, params=None, tables=None, order_by=None, select_params=None)`

Sometimes, the Django query syntax by itself can't easily express a complex
`WHERE` clause. For these edge cases, Django provides the `extra()`
`QuerySet` modifier — a hook for injecting specific clauses into the SQL
generated by a `QuerySet`.

> **Use this method as a last resort**
>
> This is an old API that we aim to deprecate at some point in the future.
> Use it only if you cannot express your query using other queryset methods.
> If you do need to use it, please [file a ticket](https://code.djangoproject.com/newticket) using the [QuerySet.extra
> keyword](https://code.djangoproject.com/query?status=assigned&status=new&keywords=~QuerySet.extra)
> with your use case (please check the list of existing tickets first) so
> that we can enhance the QuerySet API to allow removing `extra()`. We are
> no longer improving or fixing bugs for this method.
>
> For example, this use of `extra()`:
>
> ```
> >>> qs.extra(
> ...     select={'val': "select col from sometable where othercol = %s"},
> ...     select_params=(someparam,),
> ... )
> ```
>
> これは以下と同じです:
>
> ```
> >>> qs.annotate(val=RawSQL("select col from sometable where othercol = %s", (someparam,)))
> ```
>
> The main benefit of using [`RawSQL`](/ja/3.2/ref/models/expressions/#django.db.models.expressions.RawSQL) is
> that you can set `output_field` if needed. The main downside is that if
> you refer to some table alias of the queryset in the raw SQL, then it is
> possible that Django might change that alias (for example, when the
> queryset is used as a subquery in yet another query).

> **Warning**
>
> You should be very careful whenever you use `extra()`. Every time you use
> it, you should escape any parameters that the user can control by using
> `params` in order to protect against SQL injection attacks.
>
> You also must not quote placeholders in the SQL string. This example is
> vulnerable to SQL injection because of the quotes around `%s`:
>
> ```sql
> SELECT col FROM sometable WHERE othercol = '%s'  # unsafe!
> ```
>
> You can read more about how Django's [SQL injection protection](/ja/3.2/topics/security/#sql-injection-protection) works.

By definition, these extra lookups may not be portable to different database
engines (because you're explicitly writing SQL code) and violate the DRY
principle, so you should avoid them if possible.

Specify one or more of `params`, `select`, `where` or `tables`. None
of the arguments is required, but you should use at least one of them.

- `select`

  The `select` argument lets you put extra fields in the `SELECT`
  clause.  It should be a dictionary mapping attribute names to SQL
  clauses to use to calculate that attribute.

  実装例:

  ```
  Entry.objects.extra(select={'is_recent': "pub_date > '2006-01-01'"})
  ```

  As a result, each `Entry` object will have an extra attribute,
  `is_recent`, a boolean representing whether the entry's `pub_date`
  is greater than Jan. 1, 2006.

  Django inserts the given SQL snippet directly into the `SELECT`
  statement, so the resulting SQL of the above example would be something like:

  ```sql
  SELECT blog_entry.*, (pub_date > '2006-01-01') AS is_recent
  FROM blog_entry;
  ```

  The next example is more advanced; it does a subquery to give each
  resulting `Blog` object an `entry_count` attribute, an integer count
  of associated `Entry` objects:

  ```
  Blog.objects.extra(
      select={
          'entry_count': 'SELECT COUNT(*) FROM blog_entry WHERE blog_entry.blog_id = blog_blog.id'
      },
  )
  ```

  In this particular case, we're exploiting the fact that the query will
  already contain the `blog_blog` table in its `FROM` clause.

  The resulting SQL of the above example would be:

  ```sql
  SELECT blog_blog.*, (SELECT COUNT(*) FROM blog_entry WHERE blog_entry.blog_id = blog_blog.id) AS entry_count
  FROM blog_blog;
  ```

  Note that the parentheses required by most database engines around
  subqueries are not required in Django's `select` clauses. Also note
  that some database backends, such as some MySQL versions, don't support
  subqueries.

  In some rare cases, you might wish to pass parameters to the SQL
  fragments in `extra(select=...)`. For this purpose, use the
  `select_params` parameter.

  This will work, for example:

  ```
  Blog.objects.extra(
      select={'a': '%s', 'b': '%s'},
      select_params=('one', 'two'),
  )
  ```

  If you need to use a literal `%s` inside your select string, use
  the sequence `%%s`.
- `where` / `tables`

  You can define explicit SQL `WHERE` clauses — perhaps to perform
  non-explicit joins — by using `where`. You can manually add tables to
  the SQL `FROM` clause by using `tables`.

  `where` and `tables` both take a list of strings. All `where`
  parameters are "AND"ed to any other search criteria.

  実装例:

  ```
  Entry.objects.extra(where=["foo='a' OR bar = 'a'", "baz = 'a'"])
  ```

  ...translates (roughly) into the following SQL:

  ```sql
  SELECT * FROM blog_entry WHERE (foo='a' OR bar='a') AND (baz='a')
  ```

  Be careful when using the `tables` parameter if you're specifying
  tables that are already used in the query. When you add extra tables
  via the `tables` parameter, Django assumes you want that table
  included an extra time, if it is already included. That creates a
  problem, since the table name will then be given an alias. If a table
  appears multiple times in an SQL statement, the second and subsequent
  occurrences must use aliases so the database can tell them apart. If
  you're referring to the extra table you added in the extra `where`
  parameter this is going to cause errors.

  Normally you'll only be adding extra tables that don't already appear
  in the query. However, if the case outlined above does occur, there are
  a few solutions. First, see if you can get by without including the
  extra table and use the one already in the query. If that isn't
  possible, put your `extra()` call at the front of the queryset
  construction so that your table is the first use of that table.
  Finally, if all else fails, look at the query produced and rewrite your
  `where` addition to use the alias given to your extra table. The
  alias will be the same each time you construct the queryset in the same
  way, so you can rely upon the alias name to not change.
- `order_by`

  If you need to order the resulting queryset using some of the new
  fields or tables you have included via `extra()` use the `order_by`
  parameter to `extra()` and pass in a sequence of strings. These
  strings should either be model fields (as in the normal
  [`order_by()`](#django.db.models.query.QuerySet.order_by) method on querysets), of the form
  `table_name.column_name` or an alias for a column that you specified
  in the `select` parameter to `extra()`.

  例:

  ```
  q = Entry.objects.extra(select={'is_recent': "pub_date > '2006-01-01'"})
  q = q.extra(order_by = ['-is_recent'])
  ```

  This would sort all the items for which `is_recent` is true to the
  front of the result set (`True` sorts before `False` in a
  descending ordering).

  This shows, by the way, that you can make multiple calls to `extra()`
  and it will behave as you expect (adding new constraints each time).
- `params`

  The `where` parameter described above may use standard Python
  database string placeholders — `'%s'` to indicate parameters the
  database engine should automatically quote. The `params` argument is
  a list of any extra parameters to be substituted.

  実装例:

  ```
  Entry.objects.extra(where=['headline=%s'], params=['Lennon'])
  ```

  Always use `params` instead of embedding values directly into
  `where` because `params` will ensure values are quoted correctly
  according to your particular backend. For example, quotes will be
  escaped correctly.

  Bad:

  ```
  Entry.objects.extra(where=["headline='Lennon'"])
  ```

  Good:

  ```
  Entry.objects.extra(where=['headline=%s'], params=['Lennon'])
  ```

> **Warning**
>
> もしあなたが MySQL でクエリを処理する場合は、複数の型を扱う際に MySQL の暗黙的な型変換が予期しない結果をもたらす場合がある事に注意してください。もし文字列型で定義したカラムに対し、数値型の値で問い合わせた場合、MySQL は比較処理を行う前にテーブル上の全ての値の型を数値型に変換します。例えば `'abc'`、 `'def'` といった値が含まれているテーブルに対して `WHERE mycolumn=0` という条件での問い合わせを行うと、両方の行がマッチします。これを防ぐため、クエリの値を利用する前に適切な型キャストを行ってください。

#### `defer()`

#### `defer(*fields)`

In some complex data-modeling situations, your models might contain a lot of
fields, some of which could contain a lot of data (for example, text fields),
or require expensive processing to convert them to Python objects. If you are
using the results of a queryset in some situation where you don't know
if you need those particular fields when you initially fetch the data, you can
tell Django not to retrieve them from the database.

This is done by passing the names of the fields to not load to `defer()`:

```
Entry.objects.defer("headline", "body")
```

A queryset that has deferred fields will still return model instances. Each
deferred field will be retrieved from the database if you access that field
(one at a time, not all the deferred fields at once).

You can make multiple calls to `defer()`. Each call adds new fields to the
deferred set:

```
# Defers both the body and headline fields.
Entry.objects.defer("body").filter(rating=5).defer("headline")
```

The order in which fields are added to the deferred set does not matter.
Calling `defer()` with a field name that has already been deferred is
harmless (the field will still be deferred).

You can defer loading of fields in related models (if the related models are
loading via [`select_related()`](#django.db.models.query.QuerySet.select_related)) by using the standard double-underscore
notation to separate related fields:

```
Blog.objects.select_related().defer("entry__headline", "entry__body")
```

If you want to clear the set of deferred fields, pass `None` as a parameter
to `defer()`:

```
# Load all fields immediately.
my_queryset.defer(None)
```

Some fields in a model won't be deferred, even if you ask for them. You can
never defer the loading of the primary key. If you are using
[`select_related()`](#django.db.models.query.QuerySet.select_related) to retrieve related models, you shouldn't defer the
loading of the field that connects from the primary model to the related
one, doing so will result in an error.

> **Note**
>
> The `defer()` method (and its cousin, [`only()`](#django.db.models.query.QuerySet.only), below) are only for
> advanced use-cases. They provide an optimization for when you have analyzed
> your queries closely and understand *exactly* what information you need and
> have measured that the difference between returning the fields you need and
> the full set of fields for the model will be significant.
>
> Even if you think you are in the advanced use-case situation, **only use
> defer() when you cannot, at queryset load time, determine if you will need
> the extra fields or not**. If you are frequently loading and using a
> particular subset of your data, the best choice you can make is to
> normalize your models and put the non-loaded data into a separate model
> (and database table). If the columns *must* stay in the one table for some
> reason, create a model with `Meta.managed = False` (see the
> [`managed attribute`](/ja/3.2/ref/models/options/#django.db.models.Options.managed) documentation)
> containing just the fields you normally need to load and use that where you
> might otherwise call `defer()`. This makes your code more explicit to the
> reader, is slightly faster and consumes a little less memory in the Python
> process.
>
> For example, both of these models use the same underlying database table:
>
> ```
> class CommonlyUsedModel(models.Model):
>     f1 = models.CharField(max_length=10)
>
>     class Meta:
>         managed = False
>         db_table = 'app_largetable'
>
> class ManagedModel(models.Model):
>     f1 = models.CharField(max_length=10)
>     f2 = models.CharField(max_length=10)
>
>     class Meta:
>         db_table = 'app_largetable'
>
> # Two equivalent QuerySets:
> CommonlyUsedModel.objects.all()
> ManagedModel.objects.all().defer('f2')
> ```
>
> If many fields need to be duplicated in the unmanaged model, it may be best
> to create an abstract model with the shared fields and then have the
> unmanaged and managed models inherit from the abstract model.

> **Note**
>
> When calling [`save()`](/ja/3.2/ref/models/instances/#django.db.models.Model.save) for instances with
> deferred fields, only the loaded fields will be saved. See
> [`save()`](/ja/3.2/ref/models/instances/#django.db.models.Model.save) for more details.

#### `only()`

#### `only(*fields)`

The `only()` method is more or less the opposite of [`defer()`](#django.db.models.query.QuerySet.defer). You call
it with the fields that should *not* be deferred when retrieving a model.  If
you have a model where almost all the fields need to be deferred, using
`only()` to specify the complementary set of fields can result in simpler
code.

Suppose you have a model with fields `name`, `age` and `biography`. The
following two querysets are the same, in terms of deferred fields:

```
Person.objects.defer("age", "biography")
Person.objects.only("name")
```

Whenever you call `only()` it *replaces* the set of fields to load
immediately. The method's name is mnemonic: **only** those fields are loaded
immediately; the remainder are deferred. Thus, successive calls to `only()`
result in only the final fields being considered:

```
# This will defer all fields except the headline.
Entry.objects.only("body", "rating").only("headline")
```

Since `defer()` acts incrementally (adding fields to the deferred list), you
can combine calls to `only()` and `defer()` and things will behave
logically:

```
# Final result is that everything except "headline" is deferred.
Entry.objects.only("headline", "body").defer("body")

# Final result loads headline and body immediately (only() replaces any
# existing set of fields).
Entry.objects.defer("body").only("headline", "body")
```

All of the cautions in the note for the [`defer()`](#django.db.models.query.QuerySet.defer) documentation apply to
`only()` as well. Use it cautiously and only after exhausting your other
options.

Using [`only()`](#django.db.models.query.QuerySet.only) and omitting a field requested using [`select_related()`](#django.db.models.query.QuerySet.select_related)
is an error as well.

> **Note**
>
> When calling [`save()`](/ja/3.2/ref/models/instances/#django.db.models.Model.save) for instances with
> deferred fields, only the loaded fields will be saved. See
> [`save()`](/ja/3.2/ref/models/instances/#django.db.models.Model.save) for more details.

#### `using()`

#### `using(alias)`

This method is for controlling which database the `QuerySet` will be
evaluated against if you are using more than one database.  The only argument
this method takes is the alias of a database, as defined in
[`DATABASES`](/ja/3.2/ref/settings/#std-setting-DATABASES).

例:

```
# queries the database with the 'default' alias.
>>> Entry.objects.all()

# queries the database with the 'backup' alias
>>> Entry.objects.using('backup')
```

#### `select_for_update()`

#### `select_for_update(nowait=False, skip_locked=False, of=(), no_key=False)`

Returns a queryset that will lock rows until the end of the transaction,
generating a `SELECT ... FOR UPDATE` SQL statement on supported databases.

例:

```
from django.db import transaction

entries = Entry.objects.select_for_update().filter(author=request.user)
with transaction.atomic():
    for entry in entries:
        ...
```

When the queryset is evaluated (`for entry in entries` in this case), all
matched entries will be locked until the end of the transaction block, meaning
that other transactions will be prevented from changing or acquiring locks on
them.

Usually, if another transaction has already acquired a lock on one of the
selected rows, the query will block until the lock is released. If this is
not the behavior you want, call `select_for_update(nowait=True)`. This will
make the call non-blocking. If a conflicting lock is already acquired by
another transaction, [`DatabaseError`](/ja/3.2/ref/exceptions/#django.db.DatabaseError) will be raised when the
queryset is evaluated. You can also ignore locked rows by using
`select_for_update(skip_locked=True)` instead. The `nowait` and
`skip_locked` are mutually exclusive and attempts to call
`select_for_update()` with both options enabled will result in a
[`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError).

By default, `select_for_update()` locks all rows that are selected by the
query. For example, rows of related objects specified in [`select_related()`](#django.db.models.query.QuerySet.select_related)
are locked in addition to rows of the queryset's model. If this isn't desired,
specify the related objects you want to lock in `select_for_update(of=(...))`
using the same fields syntax as [`select_related()`](#django.db.models.query.QuerySet.select_related). Use the value `'self'`
to refer to the queryset's model.

> **Lock parents models in select_for_update(of=(...))**
>
> If you want to lock parents models when using [multi-table inheritance](/ja/3.2/topics/db/models/#multi-table-inheritance), you must specify parent link fields (by default
> `<parent_model_name>_ptr`) in the `of` argument. For example:
>
> ```
> Restaurant.objects.select_for_update(of=('self', 'place_ptr'))
> ```

On PostgreSQL only, you can pass `no_key=True` in order to acquire a weaker
lock, that still allows creating rows that merely reference locked rows
(through a foreign key, for example) while the lock is in place. The
PostgreSQL documentation has more details about [row-level lock modes](https://www.postgresql.org/docs/current/explicit-locking.html#LOCKING-ROWS).

You can't use `select_for_update()` on nullable relations:

```
>>> Person.objects.select_related('hometown').select_for_update()
Traceback (most recent call last):
...
django.db.utils.NotSupportedError: FOR UPDATE cannot be applied to the nullable side of an outer join
```

To avoid that restriction, you can exclude null objects if you don't care about
them:

```
>>> Person.objects.select_related('hometown').select_for_update().exclude(hometown=None)
<QuerySet [<Person: ...)>, ...]>
```

Currently, the `postgresql`, `oracle`, and `mysql` database
backends support `select_for_update()`. However, MariaDB 10.3+ supports only
the `nowait` argument and MySQL 8.0.1+ supports the `nowait`,
`skip_locked`, and `of` arguments. The `no_key` argument is supported
only on PostgreSQL.

Passing `nowait=True`, `skip_locked=True`, `no_key=True`, or `of` to
`select_for_update()` using database backends that do not support these
options, such as MySQL, raises a [`NotSupportedError`](/ja/3.2/ref/exceptions/#django.db.NotSupportedError). This
prevents code from unexpectedly blocking.

Evaluating a queryset with `select_for_update()` in autocommit mode on
backends which support `SELECT ... FOR UPDATE` is a
[`TransactionManagementError`](/ja/3.2/ref/exceptions/#django.db.transaction.TransactionManagementError) error because the
rows are not locked in that case. If allowed, this would facilitate data
corruption and could easily be caused by calling code that expects to be run in
a transaction outside of one.

Using `select_for_update()` on backends which do not support
`SELECT ... FOR UPDATE` (such as SQLite) will have no effect.
`SELECT ... FOR UPDATE` will not be added to the query, and an error isn't
raised if `select_for_update()` is used in autocommit mode.

> **Warning**
>
> Although `select_for_update()` normally fails in autocommit mode, since
> [`TestCase`](/ja/3.2/topics/testing/tools/#django.test.TestCase) automatically wraps each test in a
> transaction, calling `select_for_update()` in a `TestCase` even outside
> an [`atomic()`](/ja/3.2/topics/db/transactions/#django.db.transaction.atomic) block will (perhaps unexpectedly)
> pass without raising a `TransactionManagementError`. To properly test
> `select_for_update()` you should use
> [`TransactionTestCase`](/ja/3.2/topics/testing/tools/#django.test.TransactionTestCase).

> **Certain expressions may not be supported**
>
> PostgreSQL doesn't support `select_for_update()` with
> [`Window`](/ja/3.2/ref/models/expressions/#django.db.models.expressions.Window) expressions.

> **Changed in Django 3.2**
>
> The `no_key` argument was added.
>
> The `of` argument was allowed on MySQL 8.0.1+.

#### `raw()`

#### `raw(raw_query, params=(), translations=None, using=None)`

Takes a raw SQL query, executes it, and returns a
`django.db.models.query.RawQuerySet` instance. This `RawQuerySet` instance
can be iterated over just like a normal `QuerySet` to provide object
instances.

See the [素の SQL 文の実行](/ja/3.2/topics/db/sql/) for more information.

> **Warning**
>
> `raw()` always triggers a new query and doesn't account for previous
> filtering. As such, it should generally be called from the `Manager` or
> from a fresh `QuerySet` instance.

> **Changed in Django 3.2**
>
> The default value of the `params` argument was changed from `None` to
> an empty tuple.

### Operators that return new `QuerySet`s

Combined querysets must use the same model.

#### AND (`&`)

Combines two `QuerySet`s using the SQL `AND` operator.

The following are equivalent:

```
Model.objects.filter(x=1) & Model.objects.filter(y=2)
Model.objects.filter(x=1, y=2)
from django.db.models import Q
Model.objects.filter(Q(x=1) & Q(y=2))
```

SQL equivalent:

```sql
SELECT ... WHERE x=1 AND y=2
```

#### OR (`|`)

Combines two `QuerySet`s using the SQL `OR` operator.

The following are equivalent:

```
Model.objects.filter(x=1) | Model.objects.filter(y=2)
from django.db.models import Q
Model.objects.filter(Q(x=1) | Q(y=2))
```

SQL equivalent:

```sql
SELECT ... WHERE x=1 OR y=2
```

### Methods that do not return `QuerySet`s

The following `QuerySet` methods evaluate the `QuerySet` and return
something *other than* a `QuerySet`.

These methods do not use a cache (see [キャッシングと QuerySet](/ja/3.2/topics/db/queries/#caching-and-querysets)). Rather,
they query the database each time they're called.

#### `get()`

#### `get(*args, **kwargs)`

Returns the object matching the given lookup parameters, which should be in
the format described in [Field lookups](#id4). You should use lookups that are
guaranteed unique, such as the primary key or fields in a unique constraint.
For example:

```
Entry.objects.get(id=1)
Entry.objects.get(Q(blog=blog) & Q(entry_number=1))
```

If you expect a queryset to already return one row, you can use `get()`
without any arguments to return the object for that row:

```
Entry.objects.filter(pk=1).get()
```

If `get()` doesn't find any object, it raises a [`Model.DoesNotExist`](/ja/3.2/ref/models/class/#django.db.models.Model.DoesNotExist) exception:

```
Entry.objects.get(id=-999) # raises Entry.DoesNotExist
```

If `get()` finds more than one object, it raises a
[`Model.MultipleObjectsReturned`](/ja/3.2/ref/models/class/#django.db.models.Model.MultipleObjectsReturned) exception:

```
Entry.objects.get(name='A Duplicated Name') # raises Entry.MultipleObjectsReturned
```

Both these exception classes are attributes of the model class, and specific to
that model. If you want to handle such exceptions from several `get()` calls
for different models, you can use their generic base classes. For example, you
can use [`django.core.exceptions.ObjectDoesNotExist`](/ja/3.2/ref/exceptions/#django.core.exceptions.ObjectDoesNotExist)  to handle
[`DoesNotExist`](/ja/3.2/ref/models/class/#django.db.models.Model.DoesNotExist) exceptions from multiple models:

```
from django.core.exceptions import ObjectDoesNotExist

try:
    blog = Blog.objects.get(id=1)
    entry = Entry.objects.get(blog=blog, entry_number=1)
except ObjectDoesNotExist:
    print("Either the blog or entry doesn't exist.")
```

#### `create()`

#### `create(**kwargs)`

A convenience method for creating an object and saving it all in one step.  Thus:

```
p = Person.objects.create(first_name="Bruce", last_name="Springsteen")
```

and:

```
p = Person(first_name="Bruce", last_name="Springsteen")
p.save(force_insert=True)
```

are equivalent.

The [force\_insert](/ja/3.2/ref/models/instances/#ref-models-force-insert) parameter is documented
elsewhere, but all it means is that a new object will always be created.
Normally you won't need to worry about this. However, if your model contains a
manual primary key value that you set and if that value already exists in the
database, a call to `create()` will fail with an
[`IntegrityError`](/ja/3.2/ref/exceptions/#django.db.IntegrityError) since primary keys must be unique. Be
prepared to handle the exception if you are using manual primary keys.

#### `get_or_create()`

#### `get_or_create(defaults=None, **kwargs)`

A convenience method for looking up an object with the given `kwargs` (may be
empty if your model has defaults for all fields), creating one if necessary.

(Object, created) のタプルを返します。"Object"は受け取ったものか作られたものです。そして"created"はそのObjectが作られたものかどうかのBooleanです。

This is meant to prevent duplicate objects from being created when requests are
made in parallel, and as a shortcut to boilerplatish code. For example:

```
try:
    obj = Person.objects.get(first_name='John', last_name='Lennon')
except Person.DoesNotExist:
    obj = Person(first_name='John', last_name='Lennon', birthday=date(1940, 10, 9))
    obj.save()
```

Here, with concurrent requests, multiple attempts to save a `Person` with
the same parameters may be made. To avoid this race condition, the above
example can be rewritten using `get_or_create()` like so:

```
obj, created = Person.objects.get_or_create(
    first_name='John',
    last_name='Lennon',
    defaults={'birthday': date(1940, 10, 9)},
)
```

Any keyword arguments passed to `get_or_create()` — *except* an optional one
called `defaults` — will be used in a [`get()`](#django.db.models.query.QuerySet.get) call. If an object is
found, `get_or_create()` returns a tuple of that object and `False`.

> **Warning**
>
> This method is atomic assuming that the database enforces uniqueness of the
> keyword arguments (see [`unique`](/ja/3.2/ref/models/fields/#django.db.models.Field.unique) or
> [`unique_together`](/ja/3.2/ref/models/options/#django.db.models.Options.unique_together)). If the fields used in the
> keyword arguments do not have a uniqueness constraint, concurrent calls to
> this method may result in multiple rows with the same parameters being
> inserted.

You can specify more complex conditions for the retrieved object by chaining
`get_or_create()` with `filter()` and using [`Q objects`](#django.db.models.Q). For example, to retrieve Robert or Bob Marley if either
exists, and create the latter otherwise:

```
from django.db.models import Q

obj, created = Person.objects.filter(
    Q(first_name='Bob') | Q(first_name='Robert'),
).get_or_create(last_name='Marley', defaults={'first_name': 'Bob'})
```

If multiple objects are found, `get_or_create()` raises
[`MultipleObjectsReturned`](/ja/3.2/ref/exceptions/#django.core.exceptions.MultipleObjectsReturned). If an object is *not*
found, `get_or_create()` will instantiate and save a new object, returning a
tuple of the new object and `True`. The new object will be created roughly
according to this algorithm:

```
params = {k: v for k, v in kwargs.items() if '__' not in k}
params.update({k: v() if callable(v) else v for k, v in defaults.items()})
obj = self.model(**params)
obj.save()
```

In English, that means start with any non-`'defaults'` keyword argument that
doesn't contain a double underscore (which would indicate a non-exact lookup).
Then add the contents of `defaults`, overriding any keys if necessary, and
use the result as the keyword arguments to the model class. If there are any
callables in `defaults`, evaluate them. As hinted at above, this is a
simplification of the algorithm that is used, but it contains all the pertinent
details. The internal implementation has some more error-checking than this and
handles some extra edge-conditions; if you're interested, read the code.

If you have a field named `defaults` and want to use it as an exact lookup in
`get_or_create()`, use `'defaults__exact'`, like so:

```
Foo.objects.get_or_create(defaults__exact='bar', defaults={'defaults': 'baz'})
```

The `get_or_create()` method has similar error behavior to [`create()`](#django.db.models.query.QuerySet.create)
when you're using manually specified primary keys. If an object needs to be
created and the key already exists in the database, an
[`IntegrityError`](/ja/3.2/ref/exceptions/#django.db.IntegrityError) will be raised.

Finally, a word on using `get_or_create()` in Django views. Please make sure
to use it only in `POST` requests unless you have a good reason not to.
`GET` requests shouldn't have any effect on data. Instead, use `POST`
whenever a request to a page has a side effect on your data. For more, see
[**Safe methods**](https://datatracker.ietf.org/doc/html/rfc7231.html#section-4.2.1) in the HTTP spec.

> **Warning**
>
> You can use `get_or_create()` through [`ManyToManyField`](/ja/3.2/ref/models/fields/#django.db.models.ManyToManyField)
> attributes and reverse relations. In that case you will restrict the queries
> inside the context of that relation. That could lead you to some integrity
> problems if you don't use it consistently.
>
> Being the following models:
>
> ```
> class Chapter(models.Model):
>     title = models.CharField(max_length=255, unique=True)
>
> class Book(models.Model):
>     title = models.CharField(max_length=256)
>     chapters = models.ManyToManyField(Chapter)
> ```
>
> You can use `get_or_create()` through Book's chapters field, but it only
> fetches inside the context of that book:
>
> ```
> >>> book = Book.objects.create(title="Ulysses")
> >>> book.chapters.get_or_create(title="Telemachus")
> (<Chapter: Telemachus>, True)
> >>> book.chapters.get_or_create(title="Telemachus")
> (<Chapter: Telemachus>, False)
> >>> Chapter.objects.create(title="Chapter 1")
> <Chapter: Chapter 1>
> >>> book.chapters.get_or_create(title="Chapter 1")
> # Raises IntegrityError
> ```
>
> This is happening because it's trying to get or create "Chapter 1" through the
> book "Ulysses", but it can't do any of them: the relation can't fetch that
> chapter because it isn't related to that book, but it can't create it either
> because `title` field should be unique.

#### `update_or_create()`

#### `update_or_create(defaults=None, **kwargs)`

A convenience method for updating an object with the given `kwargs`, creating
a new one if necessary. The `defaults` is a dictionary of (field, value)
pairs used to update the object. The values in `defaults` can be callables.

(Object, created) のタプルを返します。"Object"は受け取ったものか更新したものです。そして"created"はそのObjectが更新されたものかどうかのBooleanです。

The `update_or_create` method tries to fetch an object from database based on
the given `kwargs`. If a match is found, it updates the fields passed in the
`defaults` dictionary.

これは手っ取り早い定型的なコードです。例えば:

```
defaults = {'first_name': 'Bob'}
try:
    obj = Person.objects.get(first_name='John', last_name='Lennon')
    for key, value in defaults.items():
        setattr(obj, key, value)
    obj.save()
except Person.DoesNotExist:
    new_values = {'first_name': 'John', 'last_name': 'Lennon'}
    new_values.update(defaults)
    obj = Person(**new_values)
    obj.save()
```

This pattern gets quite unwieldy as the number of fields in a model goes up.
The above example can be rewritten using `update_or_create()` like so:

```
obj, created = Person.objects.update_or_create(
    first_name='John', last_name='Lennon',
    defaults={'first_name': 'Bob'},
)
```

For a detailed description of how names passed in `kwargs` are resolved, see
[`get_or_create()`](#django.db.models.query.QuerySet.get_or_create).

As described above in [`get_or_create()`](#django.db.models.query.QuerySet.get_or_create), this method is prone to a
race-condition which can result in multiple rows being inserted simultaneously
if uniqueness is not enforced at the database level.

Like [`get_or_create()`](#django.db.models.query.QuerySet.get_or_create) and [`create()`](#django.db.models.query.QuerySet.create), if you're using manually
specified primary keys and an object needs to be created but the key already
exists in the database, an [`IntegrityError`](/ja/3.2/ref/exceptions/#django.db.IntegrityError) is raised.

#### `bulk_create()`

#### `bulk_create(objs, batch_size=None, ignore_conflicts=False)`

This method inserts the provided list of objects into the database in an
efficient manner (generally only 1 query, no matter how many objects there
are), and returns created objects as a list, in the same order as provided:

```
>>> objs = Entry.objects.bulk_create([
...     Entry(headline='This is a test'),
...     Entry(headline='This is only a test'),
... ])
```

This has a number of caveats though:

- The model's `save()` method will not be called, and the `pre_save` and
  `post_save` signals will not be sent.
- It does not work with child models in a multi-table inheritance scenario.
- If the model's primary key is an [`AutoField`](/ja/3.2/ref/models/fields/#django.db.models.AutoField), the
  primary key attribute can only be retrieved on certain databases (currently
  PostgreSQL and MariaDB 10.5+). On other databases, it will not be set.
- It does not work with many-to-many relationships.
- It casts `objs` to a list, which fully evaluates `objs` if it's a
  generator. The cast allows inspecting all objects so that any objects with a
  manually set primary key can be inserted first. If you want to insert objects
  in batches without evaluating the entire generator at once, you can use this
  technique as long as the objects don't have any manually set primary keys:

  ```
  from itertools import islice

  batch_size = 100
  objs = (Entry(headline='Test %s' % i) for i in range(1000))
  while True:
      batch = list(islice(objs, batch_size))
      if not batch:
          break
      Entry.objects.bulk_create(batch, batch_size)
  ```

The `batch_size` parameter controls how many objects are created in a single
query. The default is to create all objects in one batch, except for SQLite
where the default is such that at most 999 variables per query are used.

On databases that support it (all but Oracle), setting the `ignore_conflicts`
parameter to `True` tells the database to ignore failure to insert any rows
that fail constraints such as duplicate unique values. Enabling this parameter
disables setting the primary key on each model instance (if the database
normally supports it).

> **Warning**
>
> On MySQL and MariaDB, setting the `ignore_conflicts` parameter to
> `True` turns certain types of errors, other than duplicate key, into
> warnings. Even with Strict Mode. For example: invalid values or
> non-nullable violations. See the [MySQL documentation](https://dev.mysql.com/doc/refman/en/sql-mode.html#ignore-strict-comparison) and
> [MariaDB documentation](https://mariadb.com/kb/en/ignore/) for more details.

> **Changed in Django 3.1**
>
> Support for the fetching primary key attributes on MariaDB 10.5+ was added.

#### `bulk_update()`

#### `bulk_update(objs, fields, batch_size=None)`

This method efficiently updates the given fields on the provided model
instances, generally with one query:

```
>>> objs = [
...    Entry.objects.create(headline='Entry 1'),
...    Entry.objects.create(headline='Entry 2'),
... ]
>>> objs[0].headline = 'This is entry 1'
>>> objs[1].headline = 'This is entry 2'
>>> Entry.objects.bulk_update(objs, ['headline'])
```

[`QuerySet.update()`](#django.db.models.query.QuerySet.update) is used to save the changes, so this is more efficient
than iterating through the list of models and calling `save()` on each of
them, but it has a few caveats:

- You cannot update the model's primary key.
- Each model's `save()` method isn't called, and the
  [`pre_save`](/ja/3.2/ref/signals/#django.db.models.signals.pre_save) and
  [`post_save`](/ja/3.2/ref/signals/#django.db.models.signals.post_save) signals aren't sent.
- If updating a large number of columns in a large number of rows, the SQL
  generated can be very large. Avoid this by specifying a suitable
  `batch_size`.
- Updating fields defined on multi-table inheritance ancestors will incur an
  extra query per ancestor.
- When an individual batch contains duplicates, only the first instance in that
  batch will result in an update.

The `batch_size` parameter controls how many objects are saved in a single
query. The default is to update all objects in one batch, except for SQLite
and Oracle which have restrictions on the number of variables used in a query.

#### `count()`

#### `count()`

Returns an integer representing the number of objects in the database matching
the `QuerySet`.

実装例:

```
# Returns the total number of entries in the database.
Entry.objects.count()

# Returns the number of entries whose headline contains 'Lennon'
Entry.objects.filter(headline__contains='Lennon').count()
```

A `count()` call performs a `SELECT COUNT(*)` behind the scenes, so you
should always use `count()` rather than loading all of the record into Python
objects and calling `len()` on the result (unless you need to load the
objects into memory anyway, in which case `len()` will be faster).

Note that if you want the number of items in a `QuerySet` and are also
retrieving model instances from it (for example, by iterating over it), it's
probably more efficient to use `len(queryset)` which won't cause an extra
database query like `count()` would.

If the queryset has already been fully retrieved, `count()` will use that
length rather than perform an extra database query.

#### `in_bulk()`

#### `in_bulk(id_list=None, * (Keyword-only parameters separator (PEP 3102)), field_name='pk')`

Takes a list of field values (`id_list`) and the `field_name` for those
values, and returns a dictionary mapping each value to an instance of the
object with the given field value. No
[`django.core.exceptions.ObjectDoesNotExist`](/ja/3.2/ref/exceptions/#django.core.exceptions.ObjectDoesNotExist) exceptions will ever be raised
by `in_bulk`; that is, any `id_list` value not matching any instance will
simply be ignored. If `id_list` isn't provided, all objects
in the queryset are returned. `field_name` must be a unique field or a
distinct field (if there's only one field specified in [`distinct()`](#django.db.models.query.QuerySet.distinct)).
`field_name` defaults to the primary key.

実装例:

```
>>> Blog.objects.in_bulk([1])
{1: <Blog: Beatles Blog>}
>>> Blog.objects.in_bulk([1, 2])
{1: <Blog: Beatles Blog>, 2: <Blog: Cheddar Talk>}
>>> Blog.objects.in_bulk([])
{}
>>> Blog.objects.in_bulk()
{1: <Blog: Beatles Blog>, 2: <Blog: Cheddar Talk>, 3: <Blog: Django Weblog>}
>>> Blog.objects.in_bulk(['beatles_blog'], field_name='slug')
{'beatles_blog': <Blog: Beatles Blog>}
>>> Blog.objects.distinct('name').in_bulk(field_name='name')
{'Beatles Blog': <Blog: Beatles Blog>, 'Cheddar Talk': <Blog: Cheddar Talk>, 'Django Weblog': <Blog: Django Weblog>}
```

If you pass `in_bulk()` an empty list, you'll get an empty dictionary.

> **Changed in Django 3.2**
>
> Using a distinct field was allowed.

#### `iterator()`

#### `iterator(chunk_size=2000)`

Evaluates the `QuerySet` (by performing the query) and returns an iterator
(see [**PEP 234**](https://peps.python.org/pep-0234/)) over the results. A `QuerySet` typically caches its results
internally so that repeated evaluations do not result in additional queries. In
contrast, `iterator()` will read results directly, without doing any caching
at the `QuerySet` level (internally, the default iterator calls `iterator()`
and caches the return value). For a `QuerySet` which returns a large number of
objects that you only need to access once, this can result in better
performance and a significant reduction in memory.

Note that using `iterator()` on a `QuerySet` which has already been
evaluated will force it to evaluate again, repeating the query.

Also, use of `iterator()` causes previous `prefetch_related()` calls to be
ignored since these two optimizations do not make sense together.

Depending on the database backend, query results will either be loaded all at
once or streamed from the database using server-side cursors.

##### With server-side cursors

Oracle and [PostgreSQL](/ja/3.2/ref/databases/#postgresql-server-side-cursors) use server-side
cursors to stream results from the database without loading the entire result
set into memory.

The Oracle database driver always uses server-side cursors.

With server-side cursors, the `chunk_size` parameter specifies the number of
results to cache at the database driver level. Fetching bigger chunks
diminishes the number of round trips between the database driver and the
database, at the expense of memory.

On PostgreSQL, server-side cursors will only be used when the
[`DISABLE_SERVER_SIDE_CURSORS`](/ja/3.2/ref/settings/#std-setting-DATABASE-DISABLE_SERVER_SIDE_CURSORS)
setting is `False`. Read [Transaction pooling and server-side cursors](/ja/3.2/ref/databases/#transaction-pooling-server-side-cursors) if
you're using a connection pooler configured in transaction pooling mode. When
server-side cursors are disabled, the behavior is the same as databases that
don't support server-side cursors.

##### Without server-side cursors

MySQL doesn't support streaming results, hence the Python database driver loads
the entire result set into memory. The result set is then transformed into
Python row objects by the database adapter using the `fetchmany()` method
defined in [**PEP 249**](https://peps.python.org/pep-0249/).

SQLite can fetch results in batches using `fetchmany()`, but since SQLite
doesn't provide isolation between queries within a connection, be careful when
writing to the table being iterated over. See [Isolation when using QuerySet.iterator()](/ja/3.2/ref/databases/#sqlite-isolation) for
more information.

The `chunk_size` parameter controls the size of batches Django retrieves from
the database driver. Larger batches decrease the overhead of communicating with
the database driver at the expense of a slight increase in memory consumption.

The default value of `chunk_size`, 2000, comes from [a calculation on the
psycopg mailing list](https://www.postgresql.org/message-id/4D2F2C71.8080805%40dndg.it):

> Assuming rows of 10-20 columns with a mix of textual and numeric data, 2000
> is going to fetch less than 100KB of data, which seems a good compromise
> between the number of rows transferred and the data discarded if the loop
> is exited early.

#### `latest()`

#### `latest(*fields)`

Returns the latest object in the table based on the given field(s).

This example returns the latest `Entry` in the table, according to the
`pub_date` field:

```
Entry.objects.latest('pub_date')
```

You can also choose the latest based on several fields. For example, to select
the `Entry` with the earliest `expire_date` when two entries have the same
`pub_date`:

```
Entry.objects.latest('pub_date', '-expire_date')
```

The negative sign in `'-expire_date'` means to sort `expire_date` in
*descending* order. Since `latest()` gets the last result, the `Entry` with
the earliest `expire_date` is selected.

If your model's [Meta](/ja/3.2/topics/db/models/#meta-options) specifies
[`get_latest_by`](/ja/3.2/ref/models/options/#django.db.models.Options.get_latest_by), you can omit any arguments to
`earliest()` or `latest()`. The fields specified in
[`get_latest_by`](/ja/3.2/ref/models/options/#django.db.models.Options.get_latest_by) will be used by default.

Like [`get()`](#django.db.models.query.QuerySet.get), `earliest()` and `latest()` raise
[`DoesNotExist`](/ja/3.2/ref/models/class/#django.db.models.Model.DoesNotExist) if there is no object with the
given parameters.

Note that `earliest()` and `latest()` exist purely for convenience and
readability.

> **earliest() and latest() may return instances with null dates.**
>
> Since ordering is delegated to the database, results on fields that allow
> null values may be ordered differently if you use different databases. For
> example, PostgreSQL and MySQL sort null values as if they are higher than
> non-null values, while SQLite does the opposite.
>
> You may want to filter out null values:
>
> ```
> Entry.objects.filter(pub_date__isnull=False).latest('pub_date')
> ```

#### `earliest()`

#### `earliest(*fields)`

Works otherwise like [`latest()`](#django.db.models.query.QuerySet.latest) except
the direction is changed.

#### `first()`

#### `first()`

Returns the first object matched by the queryset, or `None` if there
is no matching object. If the `QuerySet` has no ordering defined, then the
queryset is automatically ordered by the primary key. This can affect
aggregation results as described in [Interaction with order\_by()](/ja/3.2/topics/db/aggregation/#aggregation-ordering-interaction).

実装例:

```
p = Article.objects.order_by('title', 'pub_date').first()
```

Note that `first()` is a convenience method, the following code sample is
equivalent to the above example:

```
try:
    p = Article.objects.order_by('title', 'pub_date')[0]
except IndexError:
    p = None
```

#### `last()`

#### `last()`

Works like  [`first()`](#django.db.models.query.QuerySet.first), but returns the last object in the queryset.

#### `aggregate()`

#### `aggregate(*args, **kwargs)`

Returns a dictionary of aggregate values (averages, sums, etc.) calculated over
the `QuerySet`. Each argument to `aggregate()` specifies a value that will
be included in the dictionary that is returned.

The aggregation functions that are provided by Django are described in
[Aggregation Functions](#id5) below. Since aggregates are also [query
expressions](/ja/3.2/ref/models/expressions/), you may combine aggregates with other
aggregates or values to create complex aggregates.

Aggregates specified using keyword arguments will use the keyword as the name
for the annotation. Anonymous arguments will have a name generated for them
based upon the name of the aggregate function and the model field that is being
aggregated. Complex aggregates cannot use anonymous arguments and must specify
a keyword argument as an alias.

For example, when you are working with blog entries, you may want to know the
number of authors that have contributed blog entries:

```
>>> from django.db.models import Count
>>> q = Blog.objects.aggregate(Count('entry'))
{'entry__count': 16}
```

By using a keyword argument to specify the aggregate function, you can
control the name of the aggregation value that is returned:

```
>>> q = Blog.objects.aggregate(number_of_entries=Count('entry'))
{'number_of_entries': 16}
```

For an in-depth discussion of aggregation, see [the topic guide on
Aggregation](/ja/3.2/topics/db/aggregation/).

#### `exists()`

#### `exists()`

Returns `True` if the [`QuerySet`](#django.db.models.query.QuerySet) contains any results, and `False`
if not. This tries to perform the query in the simplest and fastest way
possible, but it *does* execute nearly the same query as a normal
[`QuerySet`](#django.db.models.query.QuerySet) query.

[`exists()`](#django.db.models.query.QuerySet.exists) is useful for searches relating to both
object membership in a [`QuerySet`](#django.db.models.query.QuerySet) and to the existence of any objects in
a [`QuerySet`](#django.db.models.query.QuerySet), particularly in the context of a large [`QuerySet`](#django.db.models.query.QuerySet).

The most efficient method of finding whether a model with a unique field
(e.g. `primary_key`) is a member of a [`QuerySet`](#django.db.models.query.QuerySet) is:

```
entry = Entry.objects.get(pk=123)
if some_queryset.filter(pk=entry.pk).exists():
    print("Entry contained in queryset")
```

Which will be faster than the following which requires evaluating and iterating
through the entire queryset:

```
if entry in some_queryset:
   print("Entry contained in QuerySet")
```

And to find whether a queryset contains any items:

```
if some_queryset.exists():
    print("There is at least one object in some_queryset")
```

Which will be faster than:

```
if some_queryset:
    print("There is at least one object in some_queryset")
```

... but not by a large degree (hence needing a large queryset for efficiency
gains).

Additionally, if a `some_queryset` has not yet been evaluated, but you know
that it will be at some point, then using `some_queryset.exists()` will do
more overall work (one query for the existence check plus an extra one to later
retrieve the results) than using `bool(some_queryset)`, which retrieves the
results and then checks if any were returned.

#### `update()`

#### `update(**kwargs)`

Performs an SQL update query for the specified fields, and returns
the number of rows matched (which may not be equal to the number of rows
updated if some rows already have the new value).

For example, to turn comments off for all blog entries published in 2010,
you could do this:

```
>>> Entry.objects.filter(pub_date__year=2010).update(comments_on=False)
```

(This assumes your `Entry` model has fields `pub_date` and `comments_on`.)

You can update multiple fields — there's no limit on how many. For example,
here we update the `comments_on` and `headline` fields:

```
>>> Entry.objects.filter(pub_date__year=2010).update(comments_on=False, headline='This is old')
```

The `update()` method is applied instantly, and the only restriction on the
[`QuerySet`](#django.db.models.query.QuerySet) that is updated is that it can only update columns in the
model's main table, not on related models. You can't do this, for example:

```
>>> Entry.objects.update(blog__name='foo') # Won't work!
```

Filtering based on related fields is still possible, though:

```
>>> Entry.objects.filter(blog__id=1).update(comments_on=True)
```

You cannot call `update()` on a [`QuerySet`](#django.db.models.query.QuerySet) that has had a slice taken
or can otherwise no longer be filtered.

The `update()` method returns the number of affected rows:

```
>>> Entry.objects.filter(id=64).update(comments_on=True)
1

>>> Entry.objects.filter(slug='nonexistent-slug').update(comments_on=True)
0

>>> Entry.objects.filter(pub_date__year=2010).update(comments_on=False)
132
```

If you're just updating a record and don't need to do anything with the model
object, the most efficient approach is to call `update()`, rather than
loading the model object into memory. For example, instead of doing this:

```
e = Entry.objects.get(id=10)
e.comments_on = False
e.save()
```

...do this:

```
Entry.objects.filter(id=10).update(comments_on=False)
```

Using `update()` also prevents a race condition wherein something might
change in your database in the short period of time between loading the object
and calling `save()`.

Finally, realize that `update()` does an update at the SQL level and, thus,
does not call any `save()` methods on your models, nor does it emit the
[`pre_save`](/ja/3.2/ref/signals/#django.db.models.signals.pre_save) or
[`post_save`](/ja/3.2/ref/signals/#django.db.models.signals.post_save) signals (which are a consequence of
calling [`Model.save()`](/ja/3.2/ref/models/instances/#django.db.models.Model.save)). If you want to
update a bunch of records for a model that has a custom
[`save()`](/ja/3.2/ref/models/instances/#django.db.models.Model.save) method, loop over them and call
[`save()`](/ja/3.2/ref/models/instances/#django.db.models.Model.save), like this:

```
for e in Entry.objects.filter(pub_date__year=2010):
    e.comments_on = False
    e.save()
```

##### Ordered queryset

> **New in Django 3.2**

Chaining `order_by()` with `update()` is supported only on MariaDB and
MySQL, and is ignored for different databases. This is useful for updating a
unique field in the order that is specified without conflicts. For example:

```
Entry.objects.order_by('-number').update(number=F('number') + 1)
```

> **Note**
>
> `order_by()` clause will be ignored if it contains annotations, inherited
> fields, or lookups spanning relations.

#### `delete()`

#### `delete()`

Performs an SQL delete query on all rows in the [`QuerySet`](#django.db.models.query.QuerySet) and
returns the number of objects deleted and a dictionary with the number of
deletions per object type.

The `delete()` is applied instantly. You cannot call `delete()` on a
[`QuerySet`](#django.db.models.query.QuerySet) that has had a slice taken or can otherwise no longer be
filtered.

For example, to delete all the entries in a particular blog:

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

# Delete all the entries belonging to this Blog.
>>> Entry.objects.filter(blog=b).delete()
(4, {'weblog.Entry': 2, 'weblog.Entry_authors': 2})
```

By default, Django's [`ForeignKey`](/ja/3.2/ref/models/fields/#django.db.models.ForeignKey) emulates the SQL
constraint `ON DELETE CASCADE` — in other words, any objects with foreign
keys pointing at the objects to be deleted will be deleted along with them.
For example:

```
>>> blogs = Blog.objects.all()

# This will delete all Blogs and all of their Entry objects.
>>> blogs.delete()
(5, {'weblog.Blog': 1, 'weblog.Entry': 2, 'weblog.Entry_authors': 2})
```

このカスケードの動作は、[`ForeignKey`](/ja/3.2/ref/models/fields/#django.db.models.ForeignKey) に対する [`on_delete`](/ja/3.2/ref/models/fields/#django.db.models.ForeignKey.on_delete) 属性によってカスタマイズできます。

The `delete()` method does a bulk delete and does not call any `delete()`
methods on your models. It does, however, emit the
[`pre_delete`](/ja/3.2/ref/signals/#django.db.models.signals.pre_delete) and
[`post_delete`](/ja/3.2/ref/signals/#django.db.models.signals.post_delete) signals for all deleted objects
(including cascaded deletions).

Django needs to fetch objects into memory to send signals and handle cascades.
However, if there are no cascades and no signals, then Django may take a
fast-path and delete objects without fetching into memory. For large
deletes this can result in significantly reduced memory usage. The amount of
executed queries can be reduced, too.

ForeignKeys which are set to [`on_delete`](/ja/3.2/ref/models/fields/#django.db.models.ForeignKey.on_delete)
`DO_NOTHING` do not prevent taking the fast-path in deletion.

Note that the queries generated in object deletion is an implementation
detail subject to change.

#### `as_manager()`

#### `classmethod as_manager()`

Class method that returns an instance of [`Manager`](/ja/3.2/topics/db/managers/#django.db.models.Manager)
with a copy of the `QuerySet`’s methods. See
[QuerySet のメソッドで、マネージャを生成する](/ja/3.2/topics/db/managers/#create-manager-with-queryset-methods) for more details.

#### `explain()`

#### `explain(format=None, **options)`

Returns a string of the `QuerySet`’s execution plan, which details how the
database would execute the query, including any indexes or joins that would be
used. Knowing these details may help you improve the performance of slow
queries.

For example, when using PostgreSQL:

```
>>> print(Blog.objects.filter(title='My Blog').explain())
Seq Scan on blog  (cost=0.00..35.50 rows=10 width=12)
  Filter: (title = 'My Blog'::bpchar)
```

The output differs significantly between databases.

`explain()` is supported by all built-in database backends except Oracle
because an implementation there isn't straightforward.

The `format` parameter changes the output format from the databases's
default, which is usually text-based. PostgreSQL supports `'TEXT'`,
`'JSON'`, `'YAML'`, and `'XML'` formats. MariaDB and MySQL support
`'TEXT'` (also called `'TRADITIONAL'`) and `'JSON'` formats. MySQL
8.0.16+ also supports an improved `'TREE'` format, which is similar to
PostgreSQL's `'TEXT'` output and is used by default, if supported.

Some databases accept flags that can return more information about the query.
Pass these flags as keyword arguments. For example, when using PostgreSQL:

```
>>> print(Blog.objects.filter(title='My Blog').explain(verbose=True, analyze=True))
Seq Scan on public.blog  (cost=0.00..35.50 rows=10 width=12) (actual time=0.004..0.004 rows=10 loops=1)
  Output: id, title
  Filter: (blog.title = 'My Blog'::bpchar)
Planning time: 0.064 ms
Execution time: 0.058 ms
```

On some databases, flags may cause the query to be executed which could have
adverse effects on your database. For example, the `ANALYZE` flag supported
by MariaDB, MySQL 8.0.18+, and PostgreSQL could result in changes to data if
there are triggers or if a function is called, even for a `SELECT` query.

> **Changed in Django 3.1**
>
> Support for the `'TREE'` format on MySQL 8.0.16+ and `analyze` option
> on MariaDB and MySQL 8.0.18+ were added.

### `Field` lookups

Field lookups are how you specify the meat of an SQL `WHERE` clause. They're
specified as keyword arguments to the `QuerySet` methods [`filter()`](#django.db.models.query.QuerySet.filter),
[`exclude()`](#django.db.models.query.QuerySet.exclude) and [`get()`](#django.db.models.query.QuerySet.get).

For an introduction, see [models and database queries documentation](/ja/3.2/topics/db/queries/#field-lookups-intro).

Django's built-in lookups are listed below. It is also possible to write
[custom lookups](/ja/3.2/howto/custom-lookups/) for model fields.

As a convenience when no lookup type is provided (like in
`Entry.objects.get(id=14)`) the lookup type is assumed to be [`exact`](#std-fieldlookup-exact).

#### `exact`

Exact match. If the value provided for comparison is `None`, it will be
interpreted as an SQL `NULL` (see [`isnull`](#std-fieldlookup-isnull) for more details).

例:

```
Entry.objects.get(id__exact=14)
Entry.objects.get(id__exact=None)
```

SQL equivalents:

```sql
SELECT ... WHERE id = 14;
SELECT ... WHERE id IS NULL;
```

> **MySQL comparisons**
>
> In MySQL, a database table's "collation" setting determines whether
> `exact` comparisons are case-sensitive. This is a database setting, *not*
> a Django setting. It's possible to configure your MySQL tables to use
> case-sensitive comparisons, but some trade-offs are involved. For more
> information about this, see the [collation section](/ja/3.2/ref/databases/#mysql-collation)
> in the [databases](/ja/3.2/ref/databases/) documentation.

#### `iexact`

Case-insensitive exact match. If the value provided for comparison is `None`,
it will be interpreted as an SQL `NULL` (see [`isnull`](#std-fieldlookup-isnull) for more
details).

実装例:

```
Blog.objects.get(name__iexact='beatles blog')
Blog.objects.get(name__iexact=None)
```

SQL equivalents:

```sql
SELECT ... WHERE name ILIKE 'beatles blog';
SELECT ... WHERE name IS NULL;
```

Note the first query will match `'Beatles Blog'`, `'beatles blog'`,
`'BeAtLes BLoG'`, etc.

> **SQLite users**
>
> When using the SQLite backend and non-ASCII strings, bear in mind the
> [database note](/ja/3.2/ref/databases/#sqlite-string-matching) about string comparisons.
> SQLite does not do case-insensitive matching for non-ASCII strings.

#### `contains`

Case-sensitive containment test.

実装例:

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

SQL equivalent:

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

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

> **SQLite users**
>
> SQLite doesn't support case-sensitive `LIKE` statements; `contains`
> acts like `icontains` for SQLite. See the [database note](/ja/3.2/ref/databases/#sqlite-string-matching) for more information.

#### `icontains`

Case-insensitive containment test.

実装例:

```
Entry.objects.get(headline__icontains='Lennon')
```

SQL equivalent:

```sql
SELECT ... WHERE headline ILIKE '%Lennon%';
```

> **SQLite users**
>
> When using the SQLite backend and non-ASCII strings, bear in mind the
> [database note](/ja/3.2/ref/databases/#sqlite-string-matching) about string comparisons.

#### `in`

In a given iterable; often a list, tuple, or queryset. It's not a common use
case, but strings (being iterables) are accepted.

例:

```
Entry.objects.filter(id__in=[1, 3, 4])
Entry.objects.filter(headline__in='abc')
```

SQL equivalents:

```sql
SELECT ... WHERE id IN (1, 3, 4);
SELECT ... WHERE headline IN ('a', 'b', 'c');
```

You can also use a queryset to dynamically evaluate the list of values
instead of providing a list of literal values:

```
inner_qs = Blog.objects.filter(name__contains='Cheddar')
entries = Entry.objects.filter(blog__in=inner_qs)
```

This queryset will be evaluated as subselect statement:

```sql
SELECT ... WHERE blog.id IN (SELECT id FROM ... WHERE NAME LIKE '%Cheddar%')
```

If you pass in a `QuerySet` resulting from `values()` or `values_list()`
as the value to an `__in` lookup, you need to ensure you are only extracting
one field in the result. For example, this will work (filtering on the blog
names):

```
inner_qs = Blog.objects.filter(name__contains='Ch').values('name')
entries = Entry.objects.filter(blog__name__in=inner_qs)
```

This example will raise an exception, since the inner query is trying to
extract two field values, where only one is expected:

```
# Bad code! Will raise a TypeError.
inner_qs = Blog.objects.filter(name__contains='Ch').values('name', 'id')
entries = Entry.objects.filter(blog__name__in=inner_qs)
```

> **Performance considerations**
>
> Be cautious about using nested queries and understand your database
> server's performance characteristics (if in doubt, benchmark!). Some
> database backends, most notably MySQL, don't optimize nested queries very
> well. It is more efficient, in those cases, to extract a list of values
> and then pass that into the second query. That is, execute two queries
> instead of one:
>
> ```
> values = Blog.objects.filter(
>         name__contains='Cheddar').values_list('pk', flat=True)
> entries = Entry.objects.filter(blog__in=list(values))
> ```
>
> Note the `list()` call around the Blog `QuerySet` to force execution of
> the first query. Without it, a nested query would be executed, because
> [QuerySet は遅延評価される](/ja/3.2/topics/db/queries/#querysets-are-lazy).

#### `gt`

Greater than.

実装例:

```
Entry.objects.filter(id__gt=4)
```

SQL equivalent:

```sql
SELECT ... WHERE id > 4;
```

#### `gte`

Greater than or equal to.

#### `lt`

Less than.

#### `lte`

Less than or equal to.

#### `startswith`

Case-sensitive starts-with.

実装例:

```
Entry.objects.filter(headline__startswith='Lennon')
```

SQL equivalent:

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

SQLite doesn't support case-sensitive `LIKE` statements; `startswith` acts
like `istartswith` for SQLite.

#### `istartswith`

Case-insensitive starts-with.

実装例:

```
Entry.objects.filter(headline__istartswith='Lennon')
```

SQL equivalent:

```sql
SELECT ... WHERE headline ILIKE 'Lennon%';
```

> **SQLite users**
>
> When using the SQLite backend and non-ASCII strings, bear in mind the
> [database note](/ja/3.2/ref/databases/#sqlite-string-matching) about string comparisons.

#### `endswith`

Case-sensitive ends-with.

実装例:

```
Entry.objects.filter(headline__endswith='Lennon')
```

SQL equivalent:

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

> **SQLite users**
>
> SQLite doesn't support case-sensitive `LIKE` statements; `endswith`
> acts like `iendswith` for SQLite. Refer to the [database note](/ja/3.2/ref/databases/#sqlite-string-matching) documentation for more.

#### `iendswith`

Case-insensitive ends-with.

実装例:

```
Entry.objects.filter(headline__iendswith='Lennon')
```

SQL equivalent:

```sql
SELECT ... WHERE headline ILIKE '%Lennon'
```

> **SQLite users**
>
> When using the SQLite backend and non-ASCII strings, bear in mind the
> [database note](/ja/3.2/ref/databases/#sqlite-string-matching) about string comparisons.

#### `range`

Range test (inclusive).

実装例:

```
import datetime
start_date = datetime.date(2005, 1, 1)
end_date = datetime.date(2005, 3, 31)
Entry.objects.filter(pub_date__range=(start_date, end_date))
```

SQL equivalent:

```sql
SELECT ... WHERE pub_date BETWEEN '2005-01-01' and '2005-03-31';
```

You can use `range` anywhere you can use `BETWEEN` in SQL — for dates,
numbers and even characters.

> **Warning**
>
> Filtering a `DateTimeField` with dates won't include items on the last
> day, because the bounds are interpreted as "0am on the given date". If
> `pub_date` was a `DateTimeField`, the above expression would be turned
> into this SQL:
>
> ```sql
> SELECT ... WHERE pub_date BETWEEN '2005-01-01 00:00:00' and '2005-03-31 00:00:00';
> ```
>
> Generally speaking, you can't mix dates and datetimes.

#### `date`

For datetime fields, casts the value as date. Allows chaining additional field
lookups. Takes a date value.

実装例:

```
Entry.objects.filter(pub_date__date=datetime.date(2005, 1, 1))
Entry.objects.filter(pub_date__date__gt=datetime.date(2005, 1, 1))
```

(No equivalent SQL code fragment is included for this lookup because
implementation of the relevant query varies among different database engines.)

When [`USE_TZ`](/ja/3.2/ref/settings/#std-setting-USE_TZ) is `True`, fields are converted to the current time
zone before filtering. This requires [time zone definitions in the
database](#database-time-zone-definitions).

#### `year`

For date and datetime fields, an exact year match. Allows chaining additional
field lookups. Takes an integer year.

実装例:

```
Entry.objects.filter(pub_date__year=2005)
Entry.objects.filter(pub_date__year__gte=2005)
```

SQL equivalent:

```sql
SELECT ... WHERE pub_date BETWEEN '2005-01-01' AND '2005-12-31';
SELECT ... WHERE pub_date >= '2005-01-01';
```

(The exact SQL syntax varies for each database engine.)

When [`USE_TZ`](/ja/3.2/ref/settings/#std-setting-USE_TZ) is `True`, datetime fields are converted to the
current time zone before filtering. This requires [time zone definitions
in the database](#database-time-zone-definitions).

#### `iso_year`

For date and datetime fields, an exact ISO 8601 week-numbering year match.
Allows chaining additional field lookups. Takes an integer year.

実装例:

```
Entry.objects.filter(pub_date__iso_year=2005)
Entry.objects.filter(pub_date__iso_year__gte=2005)
```

(The exact SQL syntax varies for each database engine.)

When [`USE_TZ`](/ja/3.2/ref/settings/#std-setting-USE_TZ) is `True`, datetime fields are converted to the
current time zone before filtering. This requires [time zone definitions
in the database](#database-time-zone-definitions).

#### `month`

For date and datetime fields, an exact month match. Allows chaining additional
field lookups. Takes an integer 1 (January) through 12 (December).

実装例:

```
Entry.objects.filter(pub_date__month=12)
Entry.objects.filter(pub_date__month__gte=6)
```

SQL equivalent:

```sql
SELECT ... WHERE EXTRACT('month' FROM pub_date) = '12';
SELECT ... WHERE EXTRACT('month' FROM pub_date) >= '6';
```

(The exact SQL syntax varies for each database engine.)

When [`USE_TZ`](/ja/3.2/ref/settings/#std-setting-USE_TZ) is `True`, datetime fields are converted to the
current time zone before filtering. This requires [time zone definitions
in the database](#database-time-zone-definitions).

#### `day`

For date and datetime fields, an exact day match. Allows chaining additional
field lookups. Takes an integer day.

実装例:

```
Entry.objects.filter(pub_date__day=3)
Entry.objects.filter(pub_date__day__gte=3)
```

SQL equivalent:

```sql
SELECT ... WHERE EXTRACT('day' FROM pub_date) = '3';
SELECT ... WHERE EXTRACT('day' FROM pub_date) >= '3';
```

(The exact SQL syntax varies for each database engine.)

Note this will match any record with a pub\_date on the third day of the month,
such as January 3, July 3, etc.

When [`USE_TZ`](/ja/3.2/ref/settings/#std-setting-USE_TZ) is `True`, datetime fields are converted to the
current time zone before filtering. This requires [time zone definitions
in the database](#database-time-zone-definitions).

#### `week`

For date and datetime fields, return the week number (1-52 or 53) according
to [ISO-8601](https://en.wikipedia.org/wiki/ISO-8601), i.e., weeks start
on a Monday and the first week contains the year's first Thursday.

実装例:

```
Entry.objects.filter(pub_date__week=52)
Entry.objects.filter(pub_date__week__gte=32, pub_date__week__lte=38)
```

(No equivalent SQL code fragment is included for this lookup because
implementation of the relevant query varies among different database engines.)

When [`USE_TZ`](/ja/3.2/ref/settings/#std-setting-USE_TZ) is `True`, datetime fields are converted to the
current time zone before filtering. This requires [time zone definitions
in the database](#database-time-zone-definitions).

#### `week_day`

For date and datetime fields, a 'day of the week' match. Allows chaining
additional field lookups.

Takes an integer value representing the day of week from 1 (Sunday) to 7
(Saturday).

実装例:

```
Entry.objects.filter(pub_date__week_day=2)
Entry.objects.filter(pub_date__week_day__gte=2)
```

(No equivalent SQL code fragment is included for this lookup because
implementation of the relevant query varies among different database engines.)

Note this will match any record with a `pub_date` that falls on a Monday (day
2 of the week), regardless of the month or year in which it occurs. Week days
are indexed with day 1 being Sunday and day 7 being Saturday.

When [`USE_TZ`](/ja/3.2/ref/settings/#std-setting-USE_TZ) is `True`, datetime fields are converted to the
current time zone before filtering. This requires [time zone definitions
in the database](#database-time-zone-definitions).

#### `iso_week_day`

> **New in Django 3.1**

For date and datetime fields, an exact ISO 8601 day of the week match. Allows
chaining additional field lookups.

Takes an integer value representing the day of the week from 1 (Monday) to 7
(Sunday).

実装例:

```
Entry.objects.filter(pub_date__iso_week_day=1)
Entry.objects.filter(pub_date__iso_week_day__gte=1)
```

(No equivalent SQL code fragment is included for this lookup because
implementation of the relevant query varies among different database engines.)

Note this will match any record with a `pub_date` that falls on a Monday (day
1 of the week), regardless of the month or year in which it occurs. Week days
are indexed with day 1 being Monday and day 7 being Sunday.

When [`USE_TZ`](/ja/3.2/ref/settings/#std-setting-USE_TZ) is `True`, datetime fields are converted to the
current time zone before filtering. This requires [time zone definitions
in the database](#database-time-zone-definitions).

#### `quarter`

For date and datetime fields, a 'quarter of the year' match. Allows chaining
additional field lookups. Takes an integer value between 1 and 4 representing
the quarter of the year.

Example to retrieve entries in the second quarter (April 1 to June 30):

```
Entry.objects.filter(pub_date__quarter=2)
```

(No equivalent SQL code fragment is included for this lookup because
implementation of the relevant query varies among different database engines.)

When [`USE_TZ`](/ja/3.2/ref/settings/#std-setting-USE_TZ) is `True`, datetime fields are converted to the
current time zone before filtering. This requires [time zone definitions
in the database](#database-time-zone-definitions).

#### `time`

For datetime fields, casts the value as time. Allows chaining additional field
lookups. Takes a [`datetime.time`](https://docs.python.org/3/library/datetime.html#datetime.time) value.

実装例:

```
Entry.objects.filter(pub_date__time=datetime.time(14, 30))
Entry.objects.filter(pub_date__time__range=(datetime.time(8), datetime.time(17)))
```

(No equivalent SQL code fragment is included for this lookup because
implementation of the relevant query varies among different database engines.)

When [`USE_TZ`](/ja/3.2/ref/settings/#std-setting-USE_TZ) is `True`, fields are converted to the current time
zone before filtering. This requires [time zone definitions in the
database](#database-time-zone-definitions).

#### `hour`

For datetime and time fields, an exact hour match. Allows chaining additional
field lookups. Takes an integer between 0 and 23.

実装例:

```
Event.objects.filter(timestamp__hour=23)
Event.objects.filter(time__hour=5)
Event.objects.filter(timestamp__hour__gte=12)
```

SQL equivalent:

```sql
SELECT ... WHERE EXTRACT('hour' FROM timestamp) = '23';
SELECT ... WHERE EXTRACT('hour' FROM time) = '5';
SELECT ... WHERE EXTRACT('hour' FROM timestamp) >= '12';
```

(The exact SQL syntax varies for each database engine.)

When [`USE_TZ`](/ja/3.2/ref/settings/#std-setting-USE_TZ) is `True`, datetime fields are converted to the
current time zone before filtering. This requires [time zone definitions
in the database](#database-time-zone-definitions).

#### `minute`

For datetime and time fields, an exact minute match. Allows chaining additional
field lookups. Takes an integer between 0 and 59.

実装例:

```
Event.objects.filter(timestamp__minute=29)
Event.objects.filter(time__minute=46)
Event.objects.filter(timestamp__minute__gte=29)
```

SQL equivalent:

```sql
SELECT ... WHERE EXTRACT('minute' FROM timestamp) = '29';
SELECT ... WHERE EXTRACT('minute' FROM time) = '46';
SELECT ... WHERE EXTRACT('minute' FROM timestamp) >= '29';
```

(The exact SQL syntax varies for each database engine.)

When [`USE_TZ`](/ja/3.2/ref/settings/#std-setting-USE_TZ) is `True`, datetime fields are converted to the
current time zone before filtering. This requires [time zone definitions
in the database](#database-time-zone-definitions).

#### `second`

For datetime and time fields, an exact second match. Allows chaining additional
field lookups. Takes an integer between 0 and 59.

実装例:

```
Event.objects.filter(timestamp__second=31)
Event.objects.filter(time__second=2)
Event.objects.filter(timestamp__second__gte=31)
```

SQL equivalent:

```sql
SELECT ... WHERE EXTRACT('second' FROM timestamp) = '31';
SELECT ... WHERE EXTRACT('second' FROM time) = '2';
SELECT ... WHERE EXTRACT('second' FROM timestamp) >= '31';
```

(The exact SQL syntax varies for each database engine.)

When [`USE_TZ`](/ja/3.2/ref/settings/#std-setting-USE_TZ) is `True`, datetime fields are converted to the
current time zone before filtering. This requires [time zone definitions
in the database](#database-time-zone-definitions).

#### `isnull`

Takes either `True` or `False`, which correspond to SQL queries of
`IS NULL` and `IS NOT NULL`, respectively.

実装例:

```
Entry.objects.filter(pub_date__isnull=True)
```

SQL equivalent:

```sql
SELECT ... WHERE pub_date IS NULL;
```

> **Deprecated since Django 3.1**
>
> バージョン 3.1 で非推奨: Using non-boolean values as the right-hand side is deprecated, use `True`
> or `False` instead. In Django 4.0, the exception will be raised.

#### `regex`

Case-sensitive regular expression match.

The regular expression syntax is that of the database backend in use.
In the case of SQLite, which has no built in regular expression support,
this feature is provided by a (Python) user-defined REGEXP function, and
the regular expression syntax is therefore that of Python's `re` module.

実装例:

```
Entry.objects.get(title__regex=r'^(An?|The) +')
```

SQL equivalents:

```sql
SELECT ... WHERE title REGEXP BINARY '^(An?|The) +'; -- MySQL

SELECT ... WHERE REGEXP_LIKE(title, '^(An?|The) +', 'c'); -- Oracle

SELECT ... WHERE title ~ '^(An?|The) +'; -- PostgreSQL

SELECT ... WHERE title REGEXP '^(An?|The) +'; -- SQLite
```

Using raw strings (e.g., `r'foo'` instead of `'foo'`) for passing in the
regular expression syntax is recommended.

#### `iregex`

Case-insensitive regular expression match.

実装例:

```
Entry.objects.get(title__iregex=r'^(an?|the) +')
```

SQL equivalents:

```sql
SELECT ... WHERE title REGEXP '^(an?|the) +'; -- MySQL

SELECT ... WHERE REGEXP_LIKE(title, '^(an?|the) +', 'i'); -- Oracle

SELECT ... WHERE title ~* '^(an?|the) +'; -- PostgreSQL

SELECT ... WHERE title REGEXP '(?i)^(an?|the) +'; -- SQLite
```

### Aggregation functions

Django provides the following aggregation functions in the
`django.db.models` module. For details on how to use these
aggregate functions, see [the topic guide on aggregation](/ja/3.2/topics/db/aggregation/). See the [`Aggregate`](/ja/3.2/ref/models/expressions/#django.db.models.Aggregate)
documentation to learn how to create your aggregates.

> **Warning**
>
> SQLite can't handle aggregation on date/time fields out of the box.
> This is because there are no native date/time fields in SQLite and Django
> currently emulates these features using a text field. Attempts to use
> aggregation on date/time fields in SQLite will raise `NotSupportedError`.

> **ノート**
>
> Aggregation functions return `None` when used with an empty
> `QuerySet`. For example, the `Sum` aggregation function returns `None`
> instead of `0` if the `QuerySet` contains no entries. An exception is
> `Count`, which does return `0` if the `QuerySet` is empty.

All aggregates have the following parameters in common:

#### `expressions`

Strings that reference fields on the model, transforms of the field, or
[query expressions](/ja/3.2/ref/models/expressions/).

> **Changed in Django 3.2**
>
> Support for transforms of the field was added.

#### `output_field`

An optional argument that represents the [model field](/ja/3.2/ref/models/fields/)
of the return value

> **Note**
>
> When combining multiple field types, Django can only determine the
> `output_field` if all fields are of the same type. Otherwise, you
> must provide the `output_field` yourself.

#### `filter`

An optional [`Q object`](#django.db.models.Q) that's used to filter the
rows that are aggregated.

See [Conditional aggregation](/ja/3.2/ref/models/conditional-expressions/#conditional-aggregation) and [Filtering on annotations](/ja/3.2/topics/db/aggregation/#filtering-on-annotations) for
example usage.

#### `**extra`

Keyword arguments that can provide extra context for the SQL generated
by the aggregate.

#### `Avg`

#### `class Avg(expression, output_field=None, distinct=False, filter=None, **extra)`

Returns the mean value of the given expression, which must be numeric
unless you specify a different `output_field`.

- Default alias: `<field>__avg`
- Return type: `float` if input is `int`, otherwise same as input
  field, or `output_field` if supplied

Has one optional argument:

#### `distinct`

If `distinct=True`, `Avg` returns the mean value of unique values.
This is the SQL equivalent of `AVG(DISTINCT <field>)`. The default
value is `False`.

#### `Count`

#### `class Count(expression, distinct=False, filter=None, **extra)`

Returns the number of objects that are related through the provided
expression.

- Default alias: `<field>__count`
- Return type: `int`

Has one optional argument:

#### `distinct`

If `distinct=True`, the count will only include unique instances.
This is the SQL equivalent of `COUNT(DISTINCT <field>)`. The default
value is `False`.

#### `Max`

#### `class Max(expression, output_field=None, filter=None, **extra)`

Returns the maximum value of the given expression.

- Default alias: `<field>__max`
- Return type: same as input field, or `output_field` if supplied

#### `Min`

#### `class Min(expression, output_field=None, filter=None, **extra)`

Returns the minimum value of the given expression.

- Default alias: `<field>__min`
- Return type: same as input field, or `output_field` if supplied

#### `StdDev`

#### `class StdDev(expression, output_field=None, sample=False, filter=None, **extra)`

Returns the standard deviation of the data in the provided expression.

- Default alias: `<field>__stddev`
- Return type: `float` if input is `int`, otherwise same as input
  field, or `output_field` if supplied

Has one optional argument:

#### `sample`

By default, `StdDev` returns the population standard deviation. However,
if `sample=True`, the return value will be the sample standard deviation.

#### `Sum`

#### `class Sum(expression, output_field=None, distinct=False, filter=None, **extra)`

Computes the sum of all values of the given expression.

- Default alias: `<field>__sum`
- Return type: same as input field, or `output_field` if supplied

Has one optional argument:

#### `distinct`

If `distinct=True`, `Sum` returns the sum of unique values. This is
the SQL equivalent of `SUM(DISTINCT <field>)`. The default value is
`False`.

#### `Variance`

#### `class Variance(expression, output_field=None, sample=False, filter=None, **extra)`

Returns the variance of the data in the provided expression.

- Default alias: `<field>__variance`
- Return type: `float` if input is `int`, otherwise same as input
  field, or `output_field` if supplied

Has one optional argument:

#### `sample`

By default, `Variance` returns the population variance. However,
if `sample=True`, the return value will be the sample variance.

## Query-related tools

This section provides reference material for query-related tools not documented
elsewhere.

### `Q()` objects

#### `class Q`

A `Q()` object represents an SQL condition that can be used in
database-related operations. It's similar to how an
[`F()`](/ja/3.2/ref/models/expressions/#django.db.models.F) object represents the value of a model field
or annotation. They make it possible to define and reuse conditions, and
combine them using operators such as `|` (`OR`) and `&` (`AND`). See
[Q オブジェクトを用いた複雑な検索](/ja/3.2/topics/db/queries/#complex-lookups-with-q).

### `Prefetch()` objects

#### `class Prefetch(lookup, queryset=None, to_attr=None)`

The `Prefetch()` object can be used to control the operation of
[`prefetch_related()`](#django.db.models.query.QuerySet.prefetch_related).

The `lookup` argument describes the relations to follow and works the same
as the string based lookups passed to
[`prefetch_related()`](#django.db.models.query.QuerySet.prefetch_related). For example:

```
>>> from django.db.models import Prefetch
>>> Question.objects.prefetch_related(Prefetch('choice_set')).get().choice_set.all()
<QuerySet [<Choice: Not much>, <Choice: The sky>, <Choice: Just hacking again>]>
# This will only execute two queries regardless of the number of Question
# and Choice objects.
>>> Question.objects.prefetch_related(Prefetch('choice_set')).all()
<QuerySet [<Question: What's up?>]>
```

The `queryset` argument supplies a base `QuerySet` for the given lookup.
This is useful to further filter down the prefetch operation, or to call
[`select_related()`](#django.db.models.query.QuerySet.select_related) from the prefetched
relation, hence reducing the number of queries even further:

```
>>> voted_choices = Choice.objects.filter(votes__gt=0)
>>> voted_choices
<QuerySet [<Choice: The sky>]>
>>> prefetch = Prefetch('choice_set', queryset=voted_choices)
>>> Question.objects.prefetch_related(prefetch).get().choice_set.all()
<QuerySet [<Choice: The sky>]>
```

The `to_attr` argument sets the result of the prefetch operation to a custom
attribute:

```
>>> prefetch = Prefetch('choice_set', queryset=voted_choices, to_attr='voted_choices')
>>> Question.objects.prefetch_related(prefetch).get().voted_choices
[<Choice: The sky>]
>>> Question.objects.prefetch_related(prefetch).get().choice_set.all()
<QuerySet [<Choice: Not much>, <Choice: The sky>, <Choice: Just hacking again>]>
```

> **Note**
>
> When using `to_attr` the prefetched result is stored in a list. This can
> provide a significant speed improvement over traditional
> `prefetch_related` calls which store the cached result within a
> `QuerySet` instance.

### `prefetch_related_objects()`

#### `prefetch_related_objects(model_instances, *related_lookups)`

Prefetches the given lookups on an iterable of model instances. This is useful
in code that receives a list of model instances as opposed to a `QuerySet`;
for example, when fetching models from a cache or instantiating them manually.

Pass an iterable of model instances (must all be of the same class) and the
lookups or [`Prefetch`](#django.db.models.Prefetch) objects you want to prefetch for. For example:

```
>>> from django.db.models import prefetch_related_objects
>>> restaurants = fetch_top_restaurants_from_cache()  # A list of Restaurants
>>> prefetch_related_objects(restaurants, 'pizzas__toppings')
```

When using multiple databases with `prefetch_related_objects`, the prefetch
query will use the database associated with the model instance. This can be
overridden by using a custom queryset in a related lookup.

### `FilteredRelation()` objects

#### `class FilteredRelation(relation_name, * (Keyword-only parameters separator (PEP 3102)), condition=Q())`

#### `relation_name`

The name of the field on which you'd like to filter the relation.

#### `condition`

A [`Q`](#django.db.models.Q) object to control the filtering.

`FilteredRelation` is used with [`annotate()`](#django.db.models.query.QuerySet.annotate) to create an
`ON` clause when a `JOIN` is performed. It doesn't act on the default
relationship but on the annotation name (`pizzas_vegetarian` in example
below).

For example, to find restaurants that have vegetarian pizzas with
`'mozzarella'` in the name:

```
>>> from django.db.models import FilteredRelation, Q
>>> Restaurant.objects.annotate(
...    pizzas_vegetarian=FilteredRelation(
...        'pizzas', condition=Q(pizzas__vegetarian=True),
...    ),
... ).filter(pizzas_vegetarian__name__icontains='mozzarella')
```

If there are a large number of pizzas, this queryset performs better than:

```
>>> Restaurant.objects.filter(
...     pizzas__vegetarian=True,
...     pizzas__name__icontains='mozzarella',
... )
```

because the filtering in the `WHERE` clause of the first queryset will only
operate on vegetarian pizzas.

`FilteredRelation` doesn't support:

- [`QuerySet.only()`](#django.db.models.query.QuerySet.only) and [`prefetch_related()`](#django.db.models.query.QuerySet.prefetch_related).
- A [`GenericForeignKey`](/ja/3.2/ref/contrib/contenttypes/#django.contrib.contenttypes.fields.GenericForeignKey)
  inherited from a parent model.

> **Changed in Django 3.2**
>
> Support for nested relations was added.