# Dokumentasi Django 5.2.8

# Acuan API `QuerySet`

This document describes the details of the `QuerySet` API. It builds on the material presented in the model and database query guides, so you'll probably want to read and understand those documents before reading this one.

Throughout this reference we'll use the example blog models presented in the database query guide.

## Ketika `QuerySet` dinilai

Internally, a `QuerySet` can be constructed, filtered, sliced, and generally passed around without actually hitting the database. No database activity actually occurs until you do something to evaluate the queryset.

Anda dapat menilai `QuerySet` dalam cara berikut:

- Iteration. A `QuerySet` is iterable, and it executes its database query the first time you iterate over it. For example, this will print the headline of all entries in the database:

      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()`.

- Asynchronous iteration. A `QuerySet` can also be iterated over using `async for`:

      async for e in Entry.objects.all():
          results.append(e)

  Both synchronous and asynchronous iterators of QuerySets share the same underlying cache.

- Slicing. As explained in Membatasi QuerySet, 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. 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()` 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()`.

### Pickling `QuerySet`s

If you `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()` using the pickled `query` attribute, it will be converted to `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 `QuerySet` objects 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.

## API `QuerySet`

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. To make this work, most `QuerySet` methods return new querysets. These methods are covered in detail later in this section.

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

  ordered

    `True` if the `QuerySet` is ordered — i.e. has an `order_by()` clause or a default ordering on the model. `False` otherwise.

  db

    Basisdata yang akan digunakan jika permintaan ini dijalankan sekarang.

  Catatan

  The `query` parameter to `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.

### Metode yang mengembalikan `QuerySet` baru

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.

Catatan

These methods do not run database queries, therefore they are safe to run in asynchronous code, and do not have separate asynchronous versions.

#### `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 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` (`*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 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:

    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:

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

Catat bahwa contoh kedua lebih bersifat membatasi.

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

#### `annotate()`

annotate(*args, **kwargs)

Annotates each object in the `QuerySet` with the provided list of query expressions or `Q` objects. Each object can be annotated with:

- a simple value, via `Value()`;

- a reference to a field on the model (or any related models), via `F()`;

- a boolean, via `Q()`; or

- a result from an aggregate expression (averages, sums, etc.) 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 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.

#### `alias()`

alias(*args, **kwargs)

Same as `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()`, `exclude()`, `filter()`, `order_by()`, and `update()`. To use aliased expression with other methods (e.g. `aggregate()`), you must promote it to an annotation:

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

`filter()` and `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()` 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.

Contoh:

    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("?")

Catatan: permintaan `order_by('?')` mungkin mahal dan lambat, bergantung pada backend basisdata anda sedang gunakan.

Untuk mengurutkan berdasarkan sebuah bidang dalam sebuah model berbeda, gunakan sintaksis sama ketika anda sedang meminta lintas hubungan model. Yaitu, nama dari bidang, diikuti oleh garis bawah ganda (`__`), diikuti oleh nama dalam model baru, dan seterusnya untuk sebanyak model anda ingin gabungkan. Sebagai contoh:

    Entry.objects.order_by("blog__name", "headline")

Jika anda mencoba mengurutkan berdasarkan sebuah bidang yang hubungan ke model lain, Django akan menggunakan pengurutan awalan pada model terkait, atau diurutkan berdasarkan primary key model terkait jika tidak ada `Meta.ordering` ditentukan. Sebagai contoh, sejak model `Blog` tidak mempunyau pengurutan awalan ditentukan:

    Entry.objects.order_by("blog")

... mirip ke:

    Entry.objects.order_by("blog__id")

Jika `Blog` mempunyai `ordering = ['name']`, kemudian himpunan permintaan pertama akan mirip pada:

    Entry.objects.order_by("blog__name")

You can also order by query expressions by calling `asc()` or `desc()` on the expression:

    Entry.objects.order_by(Coalesce("summary", "headline").desc())

`asc()` dan `desc()` mempunyai argumen (`nulls_first` dan `nulls_last`) yang mengendalikan bagimana nilai-nilai null diurutkan.

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

Catatan

Itu diizinkan untuk menentukan sebuah bidang banyak-nilai untuk mengurutkan hasil dengan (sebagai contoh, sebuah bidang `ManyToManyField`, atau hubungan balikan dari sebuah bidang `ForeignKey`).

Pertimbangkan kasus ini:

    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.

Tidak ada cara menentukan apakah pengurutan harus kasus peka. Sehubungan dengan kasus-kepekaan, Django akan mengurutkan hasil bagaimanapun backend basisdata anda biasanya mengurutkan mereka.

Anda dapat mengurutkan berdasarkan sebuah bidang dirubah menjadi huruf kecil dengan `Lower` yang akan mencapai pengurutan kasus-tetap:

    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()` with no parameters.

You can tell if a query is ordered or not by checking the `QuerySet.ordered` attribute, which will be `True` if the `QuerySet` has been ordered in any way.

Setiap panggilan `order_by()` akan membersihkan urutan sebelumnya. Sebagai contoh, permintaan ini akan diurutkan berdasarkan `pub_date` dan bukan `headline`:

    Entry.objects.order_by("headline").order_by("pub_date")

Peringatan

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.

Untuk mengambil lima abrang "terakhir" dalam himpunan permintaan, anda dapat melakukan ini:

    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()`). 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()`.

Catatan

Any fields used in an `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()` query to restrict the columns selected, the columns used in any `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()` together, be careful when ordering by fields not in the `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.

Catatan

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")
    [...]

Catatan

Keep in mind that `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` 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.

Example:

    >>> 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()`:

    >>> 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 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}]>

Sedikit kebijakan yang berharga disebutkan:

- If you have a field called `foo` that is a `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).

  Sebagai contoh:

      >>> 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()`, be aware that ordering can affect the results. See the note in `distinct()` for details.

- If you use a `values()` clause after an `extra()` call, any fields defined by a `select` argument in the `extra()` must be explicitly included in the `values()` call. Any `extra()` call made after a `values()` call will have its extra selected fields ignored.

- Calling `only()` and `defer()` after `values()` doesn't make sense, so doing so will raise a `TypeError`.

- Combining transforms and aggregates requires the use of two `annotate()` calls, either explicitly or as keyword arguments to `values()`. As above, if the transform has been registered on the relevant field type the first `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'}, ...]>

Peringatan

Because `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.

Special values for `JSONField` on SQLite

Due to the way the `JSON_EXTRACT` and `JSON_TYPE` SQL functions are implemented on SQLite, and lack of the `BOOLEAN` data type, `values()` will return `True`, `False`, and `None` instead of `"true"`, `"false"`, and `"null"` strings for `JSONField` key transforms.

Changed in Django 5.2:

The `SELECT` clause generated when using `values()` was updated to respect the order of the specified `*fields` and `**expressions`.

#### `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 1-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, ...]>

Itu adalah sebuah kesalahan melewatkan dalam `flat` ketika ada lebih dari satu bidang.

You can pass `named=True` to get results as a `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.

Jika anda tidak melewatkan nilai apapun ke `values_list()`, itu akan mengembalikan semua bidang dalam model, dalam urutan mereka dinyatakan.

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`:

    >>> 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,)]>

Special values for `JSONField` on SQLite

Due to the way the `JSON_EXTRACT` and `JSON_TYPE` SQL functions are implemented on SQLite, and lack of the `BOOLEAN` data type, `values_list()` will return `True`, `False`, and `None` instead of `"true"`, `"false"`, and `"null"` strings for `JSONField` key transforms.

Changed in Django 5.2:

The `SELECT` clause generated when using `values_list()` was updated to respect the order of the specified `*fields`.

#### `dates()`

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

Returns a `QuerySet` that evaluates to a list of `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` 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.

Contoh:

    >>> 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)

Returns a `QuerySet` that evaluates to a list of `datetime.datetime` objects representing all available dates of a particular kind within the contents of the `QuerySet`.

`field_name` harus berupa nama dari `DateTimeField` model anda.

`kind` should be either `"year"`, `"month"`, `"week"`, `"day"`, `"hour"`, `"minute"`, or `"second"`. Each `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` object. If it's `None`, Django uses the current time zone. It has no effect when `USE_TZ` is `False`.

Catatan

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.

- PostgreSQL: tidak ada persyaratan (lihat Time Zones).

- Oracle: tidak ada persyaratan (lihat Choosing a Time Zone File).

- MySQL: memuat tabel zona waktu dengan mysql_tzinfo_to_sql.

#### `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`.

Contoh:

    >>> 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, 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()`, `exists()`, `order_by()`, and `values()`/`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)

Lihat `union()` untuk beberapa pembatasan.

#### `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)

Lihat `union()` untuk beberapa pembatasan.

#### `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

Dan ini adalah pencarian `select_related`:

    # 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

Anda dapat menggunakan `select_related()` dengan himpunan permintaan apapun dari obyek:

    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)

urutan dari rantaian `filter()` dan `select_related()` tidak penting. Himpunan permintaan ini adalah setara:

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

Dan anda dapat mengikuti foreign key dalam cara yang mirip untuk meminta mereka. Jika anda memiliki model-model berikut:

    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.

Anda mengacu pada `ForeignKey` apapun atau hubungan `OneToOneField` dalam daftar dari bidang-bidang dilewatkan ke `select_related()`.

You can also refer to the reverse direction of a `OneToOneField` in the list of fields passed to `select_related` — that is, you can traverse a `OneToOneField` back to the object on which the field is defined. Instead of specifying the field name, use the `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)

Returns a `QuerySet` that will automatically retrieve, in a single batch, related objects for each of the specified lookups.

This has a similar purpose to `select_related`, in that both are designed to stop the deluge of database queries that is caused by accessing related objects, but the strategy is quite different.

`select_related` works by creating an SQL join and including the fields of the related object in the `SELECT` statement. For this reason, `select_related` gets the related objects in the same database query. However, to avoid the much larger result set that would result from joining across a 'many' relationship, `select_related` is limited to single-valued relationships - foreign key and one-to-one.

`prefetch_related`, on the other hand, does a separate lookup for each relationship, and does the 'joining' in Python. This allows it to prefetch many-to-many, many-to-one, and `GenericRelation` objects which cannot be done using `select_related`, in addition to the foreign key and one-to-one relationships that are supported by `select_related`. It also supports prefetching of `GenericForeignKey`, however, the queryset for each `ContentType` must be provided in the `querysets` parameter of `GenericPrefetch`.

Sebagai contoh, andaikan anda mempunyai dua model ini:

    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()),
            )

and run:

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

Masalah dengan ini adalah bahwa setiap kali `Pizza.__str__()` bertanya untuk `self.toppings.all()` itu harus meminta basisdata, jadi `Pizza.objects.all()` akan menjalankan sebuah permintaan pada tabel Toppings untuk setiap barang dalam `QuerySet` Pizza.

Kami dapat mengurangi hanya dua permintaan menggunakan `prefetch_related`:

    >>> Pizza.objects.prefetch_related("toppings")

Ini mengartikan sebuah `self.toppings.all()` untuk setiap `Pizza`, sekarang setiap kali `self.toppings.all()` dipanggil, daripada harus pergi ke basisdata untuk barang-barang, itu akan menemukan mereka dalam pra pengambilan cache `QuerySet` yang dikumpulkan dalam sebuah permintaan tunggal.

That is, all the relevant toppings will have been fetched in a single query, and used to make `QuerySet` instances that have a pre-filled cache of the relevant results; these are then used in the `self.toppings.all()` calls.

Permintaan-permintaan tambahan dalam `prefetch_related()` dijalankan setelah `QuerySet` telah mulai dinilai dan permintaan utama telah dijalankan.

Note that there is no mechanism to prevent another database query from altering the items in between the execution of the primary query and the additional queries, which could produce an inconsistent result. For example, if a `Pizza` is deleted after the primary query has executed, its toppings will not be returned in the additional query, and it will seem like the pizza has no toppings:

    >>> Pizza.objects.prefetch_related("toppings")
    #  "Hawaiian" Pizza was deleted in another shell.
    <QuerySet [<Pizza: Hawaiian ()>, <Pizza: Seafood (prawns, smoked salmon)>]>

Jika anda mempunyai sebuah perulangan dari instance model, anda dapat mengambil atribut terkait pada instance tersebut menggunakan fungsi `prefetch_related_objects()`.

Note that the result cache of the primary `QuerySet` and all specified related objects will then be fully loaded into memory. This changes the typical behavior of a `QuerySet`, which normally tries to avoid loading all objects into memory before they are needed, even after a query has been executed in the database.

Catatan

Remember that, as always with `QuerySet` objects, any subsequent chained methods which imply a different database query will ignore previously cached results, and retrieve data using a fresh database query. So, if you write the following:

    >>> pizzas = Pizza.objects.prefetch_related("toppings")
    >>> [list(pizza.toppings.filter(spicy=True)) for pizza in pizzas]

...kemudian fakta bahwa `pizza.toppings.all()` telah di pengambilan tidak akan membantu anda. `prefetch_related('toppings')` menyisipkan `pizza.toppings.all()`, tetapi `pizza.toppings.filter()` adalah sebuah baru dan permintaan berbeda. Cache pra pengambilan tidak dapat membantu disini; faktanya itu melukai penampilan, sejak anda telah selesai sebuah permintaan basisdata yang anda belum gunakan. Jadi gunakan fitur ini dengan waspada!

Also, if you call the database-altering methods `add()`, `create()`, `remove()`, `clear()` or `set()`, on `related managers`, any prefetched cache for the relation will be cleared.

You can also use the normal join syntax to do related fields of related fields. Suppose we have an additional model to the example above:

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

Berikut ini adalah semua sah:

    >>> Restaurant.objects.prefetch_related("pizzas__toppings")

Ini akan mengambil semua pizza milik restoran, dan semua taburan melik ke pizza tersebut. Ini akan menghasilkan sejumlah 3 permintaan basisdata - satu untuk restoran, satu untuk pizza, dan satu untuk taburan.

    >>> Restaurant.objects.prefetch_related("best_pizza__toppings")

This will fetch the best pizza and all the toppings for the best pizza for each restaurant. This will be done in 3 database queries - one for the restaurants, one for the 'best pizzas', and one for the toppings.

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")

Sejak pengambilan dijalankan setelah permintaan utama (yang menyertakan penggabungan dibutuhkan oleh `select_related`), itu dapat mengenali bahwa obyek `best_pizza` telah diambil, dan itu akan melewati pengambilan kembali.

Mengikat panggilan `prefetch_related` akan mengumpulkan pencarian yang diambil. Untuk membersihkan perilaku `prefetch_related` apapun, melewatkan `None` sebagai sebuah parameter:

    >>> non_prefetched = qs.prefetch_related(None)

Satu perbedaan untuk dicatat ketika menggunakan `prefetch_related` adalah bahwa obyek dibuat oleh sebuah permintaan dapat dibagi diantara obyek-obyek berbeda yang mereka terkait pada yaitu instance model Python tunggal dapat muncul lebih dari satu titik dalam pohon dari obyek yang dikembalikan. Ini akan biasanya terjadi dengan hubungan foreign key. Khususnya perilaku ini akan tidak menjadi sebuah masalah, dan akan dalam faktanya menyimpan kedua memori dan waktu CPU.

Selagi `prefetch_related` mendukung pengambilan hubungan `GenericForeignKey`, sejumlah permintaan akan bergantung pada data. Sejak sebuah `GenericForeignKey` dapat mengacu data dalam banyak tabel, satu permiintaan per tabel acuan adalah dibutuhkan, daripada satu permintaan untuk semua barang. Ada mungkin permintaan tambahan pada tabel `ContentType` jika baris bersangkutan belum siambil.

`prefetch_related` dalam kebanyakan kasus akan diterapkan menggunakan sebuah permintaan SQL yang menggunakan penghubung 'IN'. Ini berarti bahwa untuk `QuerySet` besar sebuah klausa 'IN' besar dapat dibangkitkan, yang, bergantung pada basisdata, mungkin memiliki masalah penampilan dari miliknya sendiri ketika itu datang mengurai atau menjalankan permintaan SQL. Selalu profil untuk kasus penggunaan anda!

If you use `iterator()` to run the query, `prefetch_related()` calls will only be observed if a value for `chunk_size` is provided.

Anda dapat menggunakan obyek `Prefetch` untuk lebih lanjut mengendalikan tindakan pengambilan.

Dalam formulir termudahnya `Prefetch` setara pada pencarian berdasarkan string tradisional:

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

You can provide a custom queryset with the optional `queryset` argument. This can be used to change the default ordering of the queryset:

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

Or to call `select_related()` when applicable to reduce the number of queries even further:

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

Anda dapat juga memberikan hasil pengambilan pada atribut penyesuaian dengan pilihan argumen `to_attr`. Hasil akan disimpan langsung dalam sebuah daftar.

Ini mengizinkan mengambil hubungan sama banyak kali dengan `QuerySet` berbeda; sebagai contoh:

    >>> 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"),
    ... )

Lookups created with custom `to_attr` can still be traversed as usual by other lookups:

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

Menggunakan `to_attr` dianjurkan ketika menyaring turun hasil pengambilan ketika itu sedikit membingungkan daripada menyimpan hasil disaring dalam cache pengelola terkait:

    >>> 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()

Custom prefetching also works with single related relations like forward `ForeignKey` or `OneToOneField`. Generally you'll want to use `select_related()` for these relations, but there are a number of cases where prefetching with a custom `QuerySet` is useful:

- Anda ingin menggunakan sebuah `QuerySet` yang melakukan pengambilan lebih lanjut pada model terkait.

- Anda ingin hanya mengambil sebah sub kumpulan dari obyek-obyek terkait.

- You want to use performance optimization techniques like `deferred fields`:

      >>> 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")

Catatan

Urutan pencarian masalah.

Ambil contoh-contoh berikut:

    >>> prefetch_related("pizzas__toppings", "pizzas")

Ini bekerja meskipun itu adalah tidak berurutan karena `'pizzas__toppings'` sudah mengandung semua informasi yang dibutuhkan, oleh karena itu argumen kedua `'pizzas'` adalah sebenarnya berulang.

    >>> prefetch_related("pizzas__toppings", Prefetch("pizzas", queryset=Pizza.objects.all()))

Ini akan memunculkan sebuah `ValueError` karena dari usaha untuk menentukan kembali queryset dari sebelumnya dicari. Catat bahwa sebuah queryset tersirat telah dibuat untuk melintasi `'pizzas'` sebagai bagian dari pencarian `'pizzas__toppings'`.

    >>> prefetch_related("pizza_list__toppings", Prefetch("pizzas", to_attr="pizza_list"))

Ini akan memicu sebuah `AttributeError` karena `'pizza_list'` tidak ada ketika `'pizza_list__toppings'` sedang diolah.

Pertimbangan ini tidak terbatas untuk digunakan dari obyek `Prefetch`. Beberapa teknik tingkat lanjut mungkin membutuhkan bahwa pencarian dilakukan dalam perintah khusus untuk menghindari permintaan tambahan; oleh karena itu dianjurkan untuk selalu berhari-hari memesan argumen `prefetch_related`.

#### `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`.

Gunakan metode ini sebagai usaha terakhir

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 using the QuerySet.extra keyword 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,),
    ... )

is equivalent to:

    >>> qs.annotate(val=RawSQL("select col from sometable where othercol = %s", (someparam,)))

Keuntungan utama dari penggunaan `RawSQL` adalah bahwa anda dapat menyetel `output_field` jika dibutuhkan. kerugian utama adalah jika anda mengacu pada beberapa nama lain tabel dari queryset dalam SQL mentah, kemudian itu memungkinkan Django mungkin merubah nama lain itu (sebagai contoh, ketika queryset didunakan sebagai subpermintaan di permintaan lain lagi).

Peringatan

Anda harus sangat berhati-hati kapanpun anda menggunakan `extra()`. Setiap waktu anda menggunakan itu, anda harus meloloskan parameter apapun dimana pengguna dapat mengendalikan menggunakan `params` untuk melindungi terhadap serangan suntikan SQL.

You also must not quote placeholders in the SQL string. This example is vulnerable to SQL injection because of the quotes around `%s`:

    SELECT col FROM sometable WHERE othercol = '%s'  # unsafe!

Anda dapat membaca lebih tentang bagaimana SQL injection protection Django bekerja.

Berdasarkan pengertian, pencarian tambahan ini mungkin tidak dapat di hubungkan pada mesin basisdata lain (karena anda secara tegas menulis kode SQL) dan melanggar prinsip DRY, jadi anda harus menghindari mereka jika memungkinkan.

Tentukan satu atau lebih dari `params`, `select`, `where` atau `tables`. Tidak satupun dari argumen dibutuhkan, tetapi anda harus menggunakan setidaknya satu dari mereka.

- `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.

  Contoh:

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

  Sebagai hasil, setiap obyek `Entry` masukan mempunyai satu atribut tambahan, `is_recent`, sebuah boolean mewakili apakah `pub_date` masukan lebih besar dari 1 Januari 2006.

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

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

  Contoh selanjutnya lebih tinggi; itu melakukan subpermintaan untuk memberikan setiap obyek `Blog` hasil sebuah atribut `entry_count`, sebuah hitungan integer dari obyek `Entry` terkait .

      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:

      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.

  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.

  Ini akan bekerja, sebagai contoh:

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

  Jika anda butuh menggunakan sebuah harfiah `%s` didalam string terpilih anda, gunakan urutan `%%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.

  Contoh:

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

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

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

  Berhati-hatilan ketika menggunakan parameter `tables` jika anda sedang menentukan tabel-tabel yang sudah digunakan dalam permintaan. Ketika anda menambahkan tabel-tabel tambahan melalui parameter `table`, Django beranggapan anda ingin bahwa tabel disertakan sebuah waktu tambahan, jika itu sudah disertakan. itu membuat sebuah masalah, sejak nama tabel akan kemudian diberikan sebuah nama lain. Jika sebuah tabel muncul berulang kali dalam sebuah pernyataan SQL, kejadian kedua dan seterusnya harus menggunakan nama lain sehingga basisdata dapat membedakan mereka. Jika anda sedang mengacu pada tabel tambahan anda telah tambahkan dalam parameter `where` tambahan ini akan menyebabkan kesalahan-kesalahan.

  Secara biasa anda hanya butuh menambahkan tabel tambahan yang belum muncul dalam permintaan. Bagaimanapun, jika kasus diuraikan diatas tidak muncul, ada sedikit pemecahan. Pertama, lihat jika anda dapat mendapatkan dengan tanpa menyertakan tabel tambahan dan menggunakan satu yang sudah ada di dalam permintaan. Jika itu tidak memungkinkan, taruh panggilan `extra()` anda pada depan dari pembangunan queryset sehingga tabel anda adalah penggunaan pertama dari tabel itu. Akhirnya, jika semuanya gagal, cari permintaan dihasilkan dan tulis kembali tambahan `where` anda untuk menggunakan nama lain diberikan pada tabel tambahan anda. nama lain akan sama setiap kali anda membangun queryset dalam cara sama, jadi anda dapat bergantung pada nama lain untuk tidak berubah.

- `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()` 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()`.

  Sebagai contoh:

      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.

  Contoh:

      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.

  Buruk:

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

  Baik:

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

Peringatan

Jika anda sedang melakukan permintaan pada MySQL, catat bahwa paksaan jenis diam MySQL mungkin menyebabkan hasil tidak diharapkan ketika mencampur jenis. Jika anda meminta pada sebuah kolom jenis string, tetapi dengan sebuah nilai integer, MySQL akan memaksa jenis-jenis dari semua nilai dalam tabel ke integer sebelum melakukan perbandingan. Sebagai contoh, jika tabel anda mengandung nilai-nilai `'abc'`, `'def'` dan anda meminta untuk `WHERE mycolumn=0`, kedua baris akan cocok. Untuk mencegah ini, lakukan pembenaran typecast sebelum menggunakan nilai dalam sebuah permintaan.

#### `defer()`

defer(*fields)

Dalam beberapa keadaan permodelan-data rumit, model anda mungkin mengandung banyak bidang, beberapa diantaranya dapat mengandung banyak data (sebagai contoh, bidang teks), atau mewajibkan pengolahan mahal untuk merubah mereka jadi obyek Python. Jika anda sedang menggunakan hasil dari queryset dalam beberapa keadaan dimana anda tidak mengetahui jika bidang-bidang tertentu tersebut ketika anda menginisialisasikan mengambil data, anda dapat memberitahu Django tidak mengambil mereka dari basisdata.

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).

Catatan

Deferred fields will not lazy-load like this from asynchronous code. Instead, you will get a `SynchronousOnlyOperation` exception. If you are writing asynchronous code, you should not try to access any fields that you `defer()`.

Anda dapat membuat panggilan banyak pada `defer()`. Setiap panggilan menambahkan bidang baru ke kumpulan yang ditangguhkan:

    # 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()`) by using the standard double-underscore notation to separate related fields:

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

Jika anda ingin membersihkan kumpulan dari bidang-bidang yang ditunda, lewatkan `None` sebagai sebuah parameter pada `deferr()`.

    # 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()` 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.

Similarly, calling `defer()` (or its counterpart `only()`) including an argument from an aggregation (e.g. using the result of `annotate()`) doesn't make sense: doing so will raise an exception. The aggregated values will always be fetched into the resulting queryset.

Catatan

The `defer()` method (and its cousin, `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` 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.

Sebagai contoh, kedua model ini menggunakan tabel basisdata pokok sama:

    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.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.

Catatan

When calling `save()` for instances with deferred fields, only the loaded fields will be saved. See `save()` for more details.

#### `only()`

only(*fields)

The `only()` method is essentially the opposite of `defer()`. Only the fields passed into this method and that are not already specified as deferred are loaded immediately when the queryset is evaluated.

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 immediately.
    Entry.objects.defer("body").only("headline", "body")

All of the cautions in the note for the `defer()` documentation apply to `only()` as well. Use it cautiously and only after exhausting your other options.

Using `only()` and omitting a field requested using `select_related()` is an error as well. On the other hand, invoking `only()` without any arguments, will return every field (including annotations) fetched by the queryset.

As with `defer()`, you cannot access the non-loaded fields from asynchronous code and expect them to load. Instead, you will get a `SynchronousOnlyOperation` exception. Ensure that all fields you might access are in your `only()` call.

Catatan

When calling `save()` for instances with deferred fields, only the loaded fields will be saved. See `save()` for more details.

Catatan

When using `defer()` after `only()` the fields in `defer()` will override `only()` for fields that are listed in both.

#### `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`.

Sebagai contoh:

    # 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.

Sebagai contoh:

    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` 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`.

By default, `select_for_update()` locks all rows that are selected by the query. For example, rows of related objects specified in `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()`. 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, 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"))

Using `select_for_update(of=(...))` with specified fields

If you want to lock models and specify selected fields, e.g. using `values()`, you must select at least one field from each model in the `of` argument. Models without selected fields will not be locked.

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.

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: ...)>, ...]>

The `postgresql`, `oracle`, and `mysql` database backends support `select_for_update()`. However, MariaDB only supports the `nowait` argument, MariaDB 10.6+ also supports the `skip_locked` argument, and MySQL supports the `nowait`, `skip_locked`, and `of` arguments. The `no_key` argument is only supported 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`. This prevents code from unexpectedly blocking.

Menilai sebuah queryset dengan `select_for_update()` dalam suasana autocommit pada backend yang mendukung `SELECT ... FOR UPDATE` adalah sebuah kesalahan `TransactionManagementError` karena baris-baris tidak dikunci dalam kasus itu. jiak diizinkan, ini akan memfasilitasi kerusakan data dan dapat dengan mudah disebabkan oleh memanggil kode yang mengharapkan dijalankan dalam sebuah transaksi diluar dari satu.

Menggunakan `select_for_update()` pada backend yang tidak mendukung `SELECT ... FOR UPDATE` (seperti SQLite) tidak akan mempunyai pengaruh. `SELECT ... FOR UPDATE` tidak akan ditambahkan pada permintaan, dan sebuah kesalahan tidak dimunculkan jika `select_for_update()` digunakan dalam suasana autocommit.

Peringatan

Although `select_for_update()` normally fails in autocommit mode, since `TestCase` automatically wraps each test in a transaction, calling `select_for_update()` in a `TestCase` even outside an `atomic()` block will (perhaps unexpectedly) pass without raising a `TransactionManagementError`. To properly test `select_for_update()` you should use `TransactionTestCase`.

Pernyataan tertentu mungkin tidak didukung

PostgreSQL tidak mendukung `select_for_update()` dengan pernyataan `Window`.

#### `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.

Lihat Melakukan permintaan SQL mentah untuk informasi lebih.

Peringatan

`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.

### Penghubung yang mengembalikan``QuerySet`` baru

Queryset paduan harus menggunakan model sama.

#### AND (`&`)

Combines two `QuerySet`s using the SQL `AND` operator in a manner similar to chaining filters.

Berikut adalah setara:

    Model.objects.filter(x=1) & Model.objects.filter(y=2)
    Model.objects.filter(x=1).filter(y=2)

Setara SQL:

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

#### OR (`|`)

Memadukan dua `QuerySet` menggunakan penghubung `OR` SQL.

Berikut adalah setara:

    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))

Setara SQL:

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

`|` is not a commutative operation, as different (though equivalent) queries may be generated.

#### XOR (`^`)

Combines two `QuerySet`s using the SQL `XOR` operator. A `XOR` expression matches rows that are matched by an odd number of operands.

Berikut adalah setara:

    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))

Setara SQL:

    SELECT ... WHERE x=1 XOR y=2

Catatan

`XOR` is natively supported on MariaDB and MySQL. On other databases, `x ^ y ^ ... ^ z` is converted to an equivalent:

    (x OR y OR ... OR z) AND
    1=MOD(
        (CASE WHEN x THEN 1 ELSE 0 END) +
        (CASE WHEN y THEN 1 ELSE 0 END) +
        ...
        (CASE WHEN z THEN 1 ELSE 0 END),
        2
    )

### Metode-metode yang tidak mengembalikan `QuerySet`

Metode `QuerySet` berikut menilai `QuerySet` dan mengembalikan sesuatu selain dari `QuerySet`.

Metode-metode ini tidak menggunakan penyimpanan sementara (lihat Cache dan QuerySet). Daripada, mereka meminta basisdata setiap kali mereka dipanggil.

Because these methods evaluate the QuerySet, they are blocking calls, and so their main (synchronous) versions cannot be called from asynchronous code. For this reason, each has a corresponding asynchronous version with an `a` prefix - for example, rather than `get(…)` you can `await aget(…)`.

There is usually no difference in behavior apart from their asynchronous nature, but any differences are noted below next to each method.

#### `get()`

get(*args, **kwargs)

aget(*args, **kwargs)

Asynchronous version: `aget()`

Returns the object matching the given lookup parameters, which should be in the format described in Field lookups. 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` exception:

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

If `get()` finds more than one object, it raises a `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` to handle `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)

acreate(**kwargs)

Asynchronous version: `acreate()`

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")

dan:

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

adalah setara.

Parameter force_insert didokumentasikan ditempat lain, tetapi semua itu berarti bahwa sebuah obyek baru akan selalu dibuat. Biasanya anda tidak akan perlu khawatir tentang ini. Bagaimanapun, jika model anda mengandung nilai primary key maual yang anda setel dan jika nilai itu sudah ada dalam basisdata, panggilan pada `create()` akan gagal denga sebuah `IntegrityError` karena primary key harus unik. Bersiaplah untuk menangani pengecualian jika anda sedang menggunakan primary keys manual.

#### `get_or_create()`

get_or_create(defaults=None, **kwargs)

aget_or_create(defaults=None, **kwargs)

Asynchronous version: `aget_or_create()`

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.

Mengembalikan sebuah tuple dari `(object, created)`, dimana `object` adalah obyek diambil atau dibuat adalah sebuah boolean menentukan apakah sebuah obyek baru telah dibuat.

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()` call. If an object is found, `get_or_create()` returns a tuple of that object and `False`.

Peringatan

This method is atomic assuming that the database enforces uniqueness of the keyword arguments (see `unique` or `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`. 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`. 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()

Dalam Bahasa Inggris, itu berarti dimulai dengan apapun argumen kata kunci non-`'defaults'` yang tidak mengandung garis bawah ganda (yang akan menunjukkan sebuah pencarian bukan-persis). Kemudian tambah isi dari `defaults`, menimpa kunci apapun jika diperlukan, dan gunakan hasil sebagai argumen kata kunci pada kelas model. Jika ada callabke apapun dalam `defaults`, nilai mereka. Seperti ditunjukkan diatas, ini adalah penyerderhanaan dari algoritma yang digunakan, tetapi itu mengandung semua rincian bersangkutan. Penerapan internal mempunyai beberapa lebih pemeriksanaan-kesalahan daripada ini dan menangani beberapa kondisi-tepi tambahan; jik anda tertarik, baca kode.

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()` 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` 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 in the HTTP spec.

Peringatan

You can use `get_or_create()` through `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.

Menjadi model berikut:

    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 either: 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, create_defaults=None, **kwargs)

aupdate_or_create(defaults=None, create_defaults=None, **kwargs)

Asynchronous version: `aupdate_or_create()`

A convenience method for updating an object with the given `kwargs`, creating a new one if necessary. Both `create_defaults` and `defaults` are dictionaries of (field, value) pairs. The values in both `create_defaults` and `defaults` can be callables. `defaults` is used to update the object while `create_defaults` are used for the create operation. If `create_defaults` is not supplied, `defaults` will be used for the create operation.

Returns a tuple of `(object, created)`, where `object` is the created or updated object and `created` is a boolean specifying whether a new object was created.

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.

This is meant as a shortcut to boilerplatish code. For example:

    defaults = {"first_name": "Bob"}
    create_defaults = {"first_name": "Bob", "birthday": date(1940, 10, 9)}
    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(create_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"},
        create_defaults={"first_name": "Bob", "birthday": date(1940, 10, 9)},
    )

For a detailed description of how names passed in `kwargs` are resolved, see `get_or_create()`.

As described above in `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()` and `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` is raised.

#### `bulk_create()`

bulk_create(objs, batch_size=None, ignore_conflicts=False, update_conflicts=False, update_fields=None, unique_fields=None)

abulk_create(objs, batch_size=None, ignore_conflicts=False, update_conflicts=False, update_fields=None, unique_fields=None)

Asynchronous version: `abulk_create()`

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"),
    ...     ]
    ... )

Ini mempunyai sejumlah peringatan:

- Metode `save()` model tidak akan dipanggil, dan sinyal `pre_save` dan `post_save` tidak akan dikirim.

- Itu tidak bekerja dengan anak model dalam sebuah skenario warisan banyak-tabel.

- If the model's primary key is an `AutoField` or has a `db_default` value, and `ignore_conflicts` is `False`, the primary key attribute can only be retrieved on certain databases (currently PostgreSQL, MariaDB, and SQLite 3.35+). On other databases, it will not be set.

- Itu tidak bekerja dengan hubungan many-to-many.

- 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 as many objects in one batch as the database will allow. (SQLite and Oracle limit the number of parameters in a query.)

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.

On databases that support it (all except Oracle), setting the `update_conflicts` parameter to `True`, tells the database to update `update_fields` when a row insertion fails on conflicts. On PostgreSQL and SQLite, in addition to `update_fields`, a list of `unique_fields` that may be in conflict must be provided.

Enabling the `ignore_conflicts` parameter disables setting the primary key on each model instance (if the database normally supports it).

Peringatan

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 and MariaDB documentation for more details.

#### `bulk_update()`

bulk_update(objs, fields, batch_size=None)

abulk_update(objs, fields, batch_size=None)

Asynchronous version: `abulk_update()`

This method efficiently updates the given fields on the provided model instances, generally with one query, and returns the number of objects updated:

    >>> 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"])
    2

`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:

- Anda tidak dapat memperbaharui primary key model.

- Each model's `save()` method isn't called, and the `pre_save` and `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`.

- When updating a large number of objects, be aware that `bulk_update()` prepares all of the `WHEN` clauses for every object across all batches before executing any queries. This can require more memory than expected. To reduce memory usage, you can use an approach like this:

      from itertools import islice

      batch_size = 100
      ids_iter = range(1000)
      while ids := list(islice(ids_iter, batch_size)):
          batch = Entry.objects.filter(ids__in=ids)
          for entry in batch:
              entry.headline = f"Updated headline {entry.pk}"
          Entry.objects.bulk_update(batch, ["headline"], batch_size=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 number of objects updated returned by the function may be fewer than the number of objects passed in. This can be due to duplicate objects passed in which are updated in the same batch or race conditions such that objects are no longer present in the database.

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()

acount()

Asynchronous version: `acount()`

Mengembalikan sebuah integer mewakili sejumlah obyek dalam `QuerySet` pencocokan basisdata.

Contoh:

    # 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, *, field_name='pk')

ain_bulk(id_list=None, *, field_name='pk')

Asynchronous version: `ain_bulk()`

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` 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()`). `field_name` defaults to the primary key.

Example:

    >>> 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>}

Jika anda melewatkan `in_bulk()` sebuah daftar kosong, anda akan mendapatkan kamus kosong.

#### `iterator()`

iterator(chunk_size=None)

aiterator(chunk_size=None)

Asynchronous version: `aiterator()`

Evaluates the `QuerySet` (by performing the query) and returns an iterator (see PEP 234) over the results, or an asynchronous iterator (see PEP 492) if you call its asynchronous version `aiterator`.

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.

Catat bahwa menggunakan `iterator()` pada sebuah `QuerySet` yang sudah dinilai akan memaksa itu dinilai kembali, mengulangi permintaan.

`iterator()` is compatible with previous calls to `prefetch_related()` as long as `chunk_size` is given. Larger values will necessitate fewer queries to accomplish the prefetching at the cost of greater memory usage.

On some databases (e.g. Oracle, SQLite), the maximum number of terms in an SQL `IN` clause might be limited. Hence values below this limit should be used. (In particular, when prefetching across two or more relations, a `chunk_size` should be small enough that the anticipated number of results for each prefetched relation still falls below the limit.)

So long as the QuerySet does not prefetch any related objects, providing no value for `chunk_size` will result in Django using an implicit default of 2000.

Bergantung pada backend basisdata, hasil permintaan akan salah satu dimuat semua sekali atau dialirkan dari basisdata menggunakan kursor sisi-peladen.

##### Dengan jarum sisi-peladen

Oracle dan PostgreSQL menggunakan kursor sisi peladen untuk mengalirkan hasil dari basisdata tanpa memuat keseluruhan hasil disetel kedalam memori.

Driver basisdata Oracle selalu menggunakan jarum sisi-peladen.

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` setting is `False`. Read Menggabungkan transaksi dan kursor sisi-peladen 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.

##### Tanpa jarum sisi-peladen

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.

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 Pengucilan ketika menggunakan QuerySet.iterator() 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.

So long as the QuerySet does not prefetch any related objects, providing no value for `chunk_size` will result in Django using an implicit default of 2000, a value derived from a calculation on the psycopg mailing list:

> 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)

alatest(*fields)

Asynchronous version: `alatest()`

Mengembalikan obyek terakhir dalam tabel berdasarkan pada bidang-bidang diberikan.

Contoh ini mengembalikan `Entry` terakhir dalam tabel, menurut pada bidang `pub-date`:

    Entry.objects.latest("pub_date")

Anda dapat juga memilih terakhir berdasarkan pada beberapa bidang. Sebagai contoh, memilih `Entry` dengan `expire_date` paling awal ketika dua masukan memiliki `pub_date` sama:

    Entry.objects.latest("pub_date", "-expire_date")

Tanda negatif dalam `'-expire_date'` berarti mengurutkan `expire_date` dalam urutan menurun. Karena `latest()` mendapatkan hasil terakhir, `Entry` dengan `expire_date` paling awal yang dipilih.

If your model's Meta specifies `get_latest_by`, you can omit any arguments to `earliest()` or `latest()`. The fields specified in `get_latest_by` will be used by default.

Like `get()`, `earliest()` and `latest()` raise `DoesNotExist` if there is no object with the given parameters.

Catat bahwa adanya `earliest()` dan `latest()` semata-mata untuk kenyamanan dan mudahan membaca.

`earliest()` dan `latest()` mungkin mengembalikan instance dengan tanggal null.

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.

Anda mungkin ingin menyaring nilai-nilai null:

    Entry.objects.filter(pub_date__isnull=False).latest("pub_date")

#### `earliest()`

earliest(*fields)

aearliest(*fields)

Asynchronous version: `aearliest()`

Bekerja sebalinya seperti `latest()` kecuali arah dirubah.

#### `first()`

first()

afirst()

Asynchronous version: `afirst()`

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 Interaksi dengan order_by().

Contoh:

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

catat bahwa `first()` adalah metode nyaman, contoh kode berikut adalah setara pada contoh diatas:

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

#### `last()`

last()

alast()

Asynchronous version: `alast()`

Works like `first()`, but returns the last object in the queryset.

#### `aggregate()`

aggregate(*args, **kwargs)

aaggregate(*args, **kwargs)

Asynchronous version: `aaggregate()`

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 below. Since aggregates are also query 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
    >>> Blog.objects.aggregate(Count("entry__authors"))
    {'entry__authors__count': 16}

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

    >>> Blog.objects.aggregate(number_of_authors=Count("entry__authors"))
    {'number_of_authors': 16}

For an in-depth discussion of aggregation, see the topic guide on Aggregation.

#### `exists()`

exists()

aexists()

Asynchronous version: `aexists()`

Returns `True` if the `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` query.

`exists()` is useful for searches relating to the existence of any objects in a `QuerySet`, particularly in the context of a large `QuerySet`.

To find whether a queryset contains any items:

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

Yang akan menjadi lebih cepat daripada:

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

... tetapi bukan berdasarkan tingkatan besar (karena itu dibtuuhkan himpunan permintaan besar untuk mendapatkan efisiensi).

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.

#### `contains()`

contains(obj)

acontains(obj)

Asynchronous version: `acontains()`

Returns `True` if the `QuerySet` contains `obj`, and `False` if not. This tries to perform the query in the simplest and fastest way possible.

`contains()` is useful for checking an object membership in a `QuerySet`, particularly in the context of a large `QuerySet`.

To check whether a queryset contains a specific item:

    if some_queryset.contains(obj):
        print("Entry contained in queryset")

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

    if obj in some_queryset:
        print("Entry contained in queryset")

Like `exists()`, if `some_queryset` has not yet been evaluated, but you know that it will be at some point, then using `some_queryset.contains(obj)` will make an additional database query, generally resulting in slower overall performance.

#### `update()`

update(**kwargs)

aupdate(**kwargs)

Asynchronous version: `aupdate()`

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)

(Ini menganggap model `Entry` mempunyai bidang `pub_date` dan `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` 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)

Anda tidak dapat memanggil `update()` pada `QuerySet` yang mempunyai potongan diambil atau dapat sebalinya tidak lagi disaring.

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()

...lakukan ini:

    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()`.

MySQL does not support self-select updates

On MySQL, `QuerySet.update()` may execute a `SELECT` followed by an `UPDATE` instead of a single `UPDATE` when filtering on related tables, which can introduce a race condition if concurrent changes occur between the queries. To ensure atomicity, consider using transactions or avoiding such filter conditions on MySQL.

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` or `post_save` signals (which are a consequence of calling `Model.save()`). If you want to update a bunch of records for a model that has a custom `save()` method, loop over them and call `save()`, like this:

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

##### Ordered queryset

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)

Catatan

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

#### `delete()`

delete()

adelete()

Asynchronous version: `adelete()`

Performs an SQL delete query on all rows in the `QuerySet` and returns the number of objects deleted and a dictionary with the number of deletions per object type.

`delete()` diberlakukan cepat. Anda tidak dapat memanggil `delete()` pada sebuah `QuerySet` yang mempunyai potongan diambil atau dapat sebaliknya tidak lagi disaring.

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, {'blog.Entry': 2, 'blog.Entry_authors': 2})

By default, Django's `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, {'blog.Blog': 1, 'blog.Entry': 2, 'blog.Entry_authors': 2})

Kebiasaan turunan ini dapat di sesuaikan melalui argumen `on_delete` ke `ForeignKey`.

The `delete()` method does a bulk delete and does not call any `delete()` methods on your models. It does, however, emit the `pre_delete` and `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` `DO_NOTHING` do not prevent taking the fast-path in deletion.

Catat bahwa permintaan dibangkitkan dalam penghapusan obyek adalah sebuah subyek rincian penerapan untuk merubah.

#### `as_manager()`

classmethod as_manager()

Class method that returns an instance of `Manager` with a copy of the `QuerySet`’s methods. See Membuat pengelola dengan cara QuerySet for more details.

Note that unlike the other entries in this section, this does not have an asynchronous variant as it does not execute a query.

#### `explain()`

explain(format=None, **options)

aexplain(format=None, **options)

Asynchronous version: `aexplain()`

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)

Keluaran berbeda secara penting diantara dua basisdata.

`explain()` didukung oleh semua backend basisdata siap-pakai kecuali oracle karena sebuah penerapan tidak ada langsung.

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 5.2:

Support for the `memory` and `serialize` options on PostgreSQL 17+ was added.

### Pencarian `Field`

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

Untuk sebuah perkenalan, lihat models and database queries documentation 1.

Pencarian siap-pakai Dajngo didaftarkan dibawah. Itu juga memungkinkan menulis custom lookups untuk bidang-bidang model.

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

#### `exact`

Pencocokan tepat. Jika nilai disediakan untuk perbandingan adalah `None`, itu akan diartikan sebagai sebuah SQL `NULL` (lihat `isnull` untuk rincian lebih).

Contoh:

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

SQL equivalents:

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

Perbandingan MySQL

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 in the 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` for more details).

Contoh:

    Blog.objects.get(name__iexact="beatles blog")
    Blog.objects.get(name__iexact=None)

SQL equivalents:

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

Catat permintaan pertama akan cocok `'Beatles Blog'`, `'beatles blog'`, `'BeAtLes BLoG'`, dll.

Pengguna SQLite

When using the SQLite backend and non-ASCII strings, bear in mind the database note about string comparisons. SQLite does not do case-insensitive matching for non-ASCII strings.

#### `contains`

Case-sensitive containment test.

Contoh:

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

Setara SQL:

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

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

Pengguna SQLite

SQLite doesn't support case-sensitive `LIKE` statements; `contains` acts like `icontains` for SQLite. See the database note for more information.

#### `icontains`

Case-insensitive containment test.

Contoh:

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

Setara SQL:

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

Pengguna SQLite

When using the SQLite backend and non-ASCII strings, bear in mind the database note 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.

Contoh:

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

SQL equivalents:

    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:

    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)

Contoh ini akan memunculkan sebuah pengecualian, sejak permintaan paling dalam mencoba mengeluarkan nilai dua bidang, dimana hanya satu yang diharapkan:

    # 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)

Pertimbangan penampilan

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 adalah lazy.

#### `gt`

Lebih besar dari

Contoh:

    Entry.objects.filter(id__gt=4)

Setara SQL:

    SELECT ... WHERE id > 4;

#### `gte`

Lebih besar dari atau sama dengan.

#### `lt`

Kurang dari

#### `lte`

Kurang dari atau sama dengan.

#### `startswith`

Dimulai-dengan kasus-peka.

Contoh:

    Entry.objects.filter(headline__startswith="Lennon")

Setara SQL:

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

SQLite tidak mendukung kasus-peka pernyataan `LIKE`; `startswith` bertindak seperti `istartswith` untuk SQLite.

#### `istartswith`

Dimulai-dengan kasus-tidak-peka.

Contoh:

    Entry.objects.filter(headline__istartswith="Lennon")

Setara SQL:

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

Pengguna SQLite

When using the SQLite backend and non-ASCII strings, bear in mind the database note about string comparisons.

#### `endswith`

Diakhiri-dengan kasus-peka.

Contoh:

    Entry.objects.filter(headline__endswith="Lennon")

Setara SQL:

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

Pengguna SQLite

SQLite doesn't support case-sensitive `LIKE` statements; `endswith` acts like `iendswith` for SQLite. Refer to the database note documentation for more.

#### `iendswith`

Diakhiri-dengan kasus-tidak-peka.

Contoh:

    Entry.objects.filter(headline__iendswith="Lennon")

Setara SQL:

    SELECT ... WHERE headline ILIKE '%Lennon'

Pengguna SQLite

When using the SQLite backend and non-ASCII strings, bear in mind the database note about string comparisons.

#### `range`

Jangkauan percobaan (termasuk).

Contoh:

    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))

Setara SQL:

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

Anda dapat menggunakan `range` dimanapun anda dapat gunakan `BETWEEN` dalam SQL -- untuk tanggal, angka bahkan karakter.

Peringatan

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:

    SELECT ... WHERE pub_date BETWEEN '2005-01-01 00:00:00' and '2005-03-31 00:00:00';

Secara umum, anda tidak dapat mencampur tanggal dan datetime.

#### `date`

Untuk bidang datetime, melemparkan nilai sebagai tanggal. Mengizinkan mengikat pencarian bidang tambahan. Mengambil nilai data.

Contoh:

    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` is `True`, fields are converted to the current time zone before filtering. This requires time zone definitions in the database.

#### `year`

Untuk bidang tanggal dan datetime, sebuah kecocokan tanggal tepat. Mengizinkan mengikat tambahan bidang pencarian. Mengambil sebuah tahun integer.

Contoh:

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

Setara SQL:

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

(Sintaksis SQL tepat beragam untuk setiap mesin basisdata.)

When `USE_TZ` is `True`, datetime fields are converted to the current time zone before filtering. This requires time zone definitions in the database.

#### `iso_year`

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

Contoh:

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

(Sintaksis SQL tepat beragam untuk setiap mesin basisdata.)

When `USE_TZ` is `True`, datetime fields are converted to the current time zone before filtering. This requires time zone definitions in the database.

#### `month`

Untuk bidang tanggal dan datetime, sebuah kecocokan bulan yang tepat. Mengizinkan mengikat bidang pencarian tambahan. Mengambil sebuah integer 1 (Januari) sampai 12 (Desember).

Contoh:

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

Setara SQL:

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

(Sintaksis SQL tepat beragam untuk setiap mesin basisdata.)

When `USE_TZ` is `True`, datetime fields are converted to the current time zone before filtering. This requires time zone definitions in the database.

#### `day`

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

Contoh:

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

Setara SQL:

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

(Sintaksis SQL tepat beragam untuk setiap mesin basisdata.)

Catat ini akan cocok dengan rekaman apapun dengan pub_date pada ahri ketiga dari bulan, seperti 3 Januari, 3 Juli, dll.

When `USE_TZ` is `True`, datetime fields are converted to the current time zone before filtering. This requires time zone definitions in the database.

#### `week`

For date and datetime fields, return the week number (1-52 or 53) according to ISO-8601, i.e., weeks start on a Monday and the first week contains the year's first Thursday.

Contoh:

    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` is `True`, datetime fields are converted to the current time zone before filtering. This requires time zone definitions in the database.

#### `week_day`

Untuk bidang tanggal dan datetime, pencocokan 'hari dari minggu'. Mengizinkan mengikat bidang pencarian tambahan.

Mengambil nilai integer mewkili hari dari minggu dari 1 (Minggu) sampai 7 (Sabtu).

Contoh:

    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` is `True`, datetime fields are converted to the current time zone before filtering. This requires time zone definitions in the database.

#### `iso_week_day`

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).

Contoh:

    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` is `True`, datetime fields are converted to the current time zone before filtering. This requires time zone definitions in the database.

#### `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.

Contoh mengambil masukan dalam perempat kedua (1 April sampai 30 Juni):

    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` is `True`, datetime fields are converted to the current time zone before filtering. This requires time zone definitions in the database.

#### `time`

Untuk bidang datetime, melemparkan nilai sebagai waktu. Mengizinkan pencarian bidang tambahan. Mengambil nilai `datetime.time`.

Contoh:

    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` is `True`, fields are converted to the current time zone before filtering. This requires time zone definitions in the database.

#### `hour`

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

Contoh:

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

Setara SQL:

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

(Sintaksis SQL tepat beragam untuk setiap mesin basisdata.)

When `USE_TZ` is `True`, datetime fields are converted to the current time zone before filtering. This requires time zone definitions in the database.

#### `minute`

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

Contoh:

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

Setara SQL:

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

(Sintaksis SQL tepat beragam untuk setiap mesin basisdata.)

When `USE_TZ` is `True`, datetime fields are converted to the current time zone before filtering. This requires time zone definitions in the database.

#### `second`

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

Contoh:

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

Setara SQL:

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

(Sintaksis SQL tepat beragam untuk setiap mesin basisdata.)

When `USE_TZ` is `True`, datetime fields are converted to the current time zone before filtering. This requires time zone definitions in the database.

#### `isnull`

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

Contoh:

    Entry.objects.filter(pub_date__isnull=True)

Setara SQL:

    SELECT ... WHERE pub_date IS NULL;

#### `regex`

Pencocokan regular expression kasus-peka.

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.

Contoh:

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

SQL equivalents:

    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

Menggunakan string mentah (misalnya, `r'foo'` daripada `'foo'`) untuk melewatkan sintaksis regular expression adalah dianjurkan.

#### `iregex`

Pencocokan regular expression kasus-tidak-peka.

Contoh:

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

SQL equivalents:

    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

### Fungsi-fungsi pengumpulan

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. See the `Aggregate` documentation to learn how to create your aggregates.

Peringatan

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`.

Empty querysets or groups

Aggregation functions return `None` when used with an empty `QuerySet` or group. For example, the `Sum` aggregation function returns `None` instead of `0` if the `QuerySet` contains no entries or for any empty group in a non-empty `QuerySet`. To return another value instead, define the `default` argument. `Count` is an exception to this behavior; it returns `0` if the `QuerySet` is empty since `Count` does not support the `default` argument.

Semua pengumpulan mempunyai parameter berikut secara umum:

#### `expressions`

Strings that reference fields on the model, transforms of the field, or query expressions.

#### `output_field`

Sebuah argumen pilihan yang mewakili model field 1 dari nilai balikan

Catatan

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`

Sebuah pilihan `Q object` yang digunakan untuk menyaring baris yang dikumpulkan.

Lihat Pengumpulan bersyarat and Penyaringan pada keterangan untuk contoh penggunaan.

#### `default`

An optional argument that allows specifying a value to use as a default value when the queryset (or grouping) contains no entries.

#### `**extra`

Argumen kata kunci yang dapat menyediakan isi tambahan untuk SQL dibangkitkan dengan pengumpulan.

#### `AnyValue`

New in Django 6.0.

class AnyValue(expression, output_field=None, filter=None, default=None, **extra)

  Returns an arbitrary value from the non-null input values.

  - Default alias: `<field>__anyvalue`

  - Return type: same as input field, or `output_field` if supplied. If the queryset or grouping is empty, `default` is returned.

  Usage example:

      >>> # Get average rating for each year along with a sample headline
      >>> # from that year.
      >>> from django.db.models import AnyValue, Avg, F, Q
      >>> sample_headline = AnyValue("headline")
      >>> Entry.objects.values(
      ...     pub_year=F("pub_date__year"),
      ... ).annotate(
      ...     avg_rating=Avg("rating"),
      ...     sample_headline=sample_headline,
      ... )

      >>> # Get a sample headline from each year with rating greater than 4.5.
      >>> sample_headline = AnyValue(
      ...     "headline",
      ...     filter=Q(rating__gt=4.5),
      ... )
      >>> Entry.objects.values(
      ...     pub_year=F("pub_date__year"),
      ... ).annotate(
      ...     avg_rating=Avg("rating"),
      ...     sample_headline=sample_headline,
      ... )

  Supported on SQLite, MySQL, Oracle, and PostgreSQL 16+.

  MySQL with `ONLY_FULL_GROUP_BY` enabled

  When the `ONLY_FULL_GROUP_BY` SQL mode is enabled on MySQL it may be necessary to use `AnyValue` if an aggregation includes a mix of aggregate and non-aggregate functions. Using `AnyValue` allows the non-aggregate function to be referenced in the select list when database cannot determine that it is functionally dependent on the columns in the group by clause. See the aggregation documentation for more details.

#### `Avg`

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

  Mengembalikan arti nilai dari pernyataan yang diberikan, yang harus berupa numerik meskipun anda menentukan sebuah `output_field` berbeda.

  - Nama lain awalan: `1__avg`

  - Return type: `float` if input is `int`, otherwise same as input field, or `output_field` if supplied. If the queryset or grouping is empty, `default` is returned.

  distinct

    Optional. 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. `Count('*')` is equivalent to the SQL `COUNT(*)` expression.

  - Nama lain awalan: `1__count`

  - Jenis kembalian: `int`

  distinct

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

  Catatan

  The `default` argument is not supported.

#### `Max`

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

  Mengembalikan nilai maksimal dari pernyataan yang diberikan.

  - Nama lain awalan: `1__max`

  - Return type: same as input field, or `output_field` if supplied. If the queryset or grouping is empty, `default` is returned.

#### `Min`

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

  Mengembalikan nilai minimal dari pernyataan yang diberikan.

  - Nama lain awalan: `1__min`

  - Return type: same as input field, or `output_field` if supplied. If the queryset or grouping is empty, `default` is returned.

#### `StdDev`

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

  Mengembalikan selisih standar dari data dalam pernyataan yang disediakan.

  - Nama lain awalan: `1__stddev`

  - Return type: `float` if input is `int`, otherwise same as input field, or `output_field` if supplied. If the queryset or grouping is empty, `default` is returned.

  sample

    Optional. 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, default=None, **extra)

  Menghitung jumlah dari semua nilai dari pernyataan yang diberikan.

  - Nama lain awalan: `1__sum`

  - Return type: same as input field, or `output_field` if supplied. If the queryset or grouping is empty, `default` is returned.

  distinct

    Optional. 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, default=None, **extra)

  Mengembalikan beragam dari data di pernyataan yang disediakan.

  - Nama lain awalan: `1__variance`

  - Return type: `float` if input is `int`, otherwise same as input field, or `output_field` if supplied. If the queryset or grouping is empty, `default` is returned.

  sample

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

#### `StringAgg`

New in Django 6.0.

class StringAgg(expression, delimiter, output_field=None, distinct=False, filter=None, order_by=None, default=None, **extra)

  Returns the input values concatenated into a string, separated by the `delimiter` string, or `default` if there are no values.

  - Default alias: `<field>__stringagg`

  - Return type: `string` or `output_field` if supplied. If the queryset or grouping is empty, `default` is returned.

  delimiter

    A `Value` or expression representing the string that should separate each of the values. For example, `Value(",")`.

## Alat-alat terkait-permintaan

Bagian ini menyediakan bahanacuan untuk alat-alat terkait-permintaan tidak didokumentasikan dimanapun.

### Obyek `Q()`

class Q

A `Q()` object represents an SQL condition that can be used in database-related operations. It's similar to how an `F()` object represents the value of a model field or annotation. They make it possible to define and reuse conditions. These can be negated using the `~` (`NOT`) operator, and combined using operators such as `|` (`OR`), `&` (`AND`), and `^` (`XOR`). See Pencarian rumit dengan obyek Q.

### Obyek `Prefetch()`

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

The `Prefetch()` object can be used to control the operation of `prefetch_related()`.

The `lookup` argument describes the relations to follow and works the same as the string based lookups passed to `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"))
    <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()` 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>]>

Argumen `to_attr` menyetel hasil daro tindakan pengambilan pada atribut penyesuaian:

    >>> 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>]>

Catatan

Ketika menggunakan `to_attr` hasil pengambilan disimpan dalam sebuah daftar. Ini dapat menyediakan peningkatan kecepatan yang signifikan terhadap panggilan `prefetch_related` tradisional yang menyimpan hasil cache dalam sebuah instance `QuerySet`.

### `prefetch_related_objects()`

prefetch_related_objects(model_instances, *related_lookups)

aprefetch_related_objects(model_instances, *related_lookups)

Asynchronous version: `aprefetch_related_objects()`

Pengambilan pencarian diberikan pada perulangan instance model. Ini berguna dalam kode yang menerima sebuah daftar dari instance model sebagai lawan pada sebuah `QuerySet`; sebagai contoh, ketika mengambil model-model dari sebuah cache atau menginstansiasi mereka manual.

Pass an iterable of model instances (must all be of the same class and able to be iterated multiple times) and the lookups or `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.

### Obyek `FilteredRelation()`

class FilteredRelation(relation_name, *, condition=Q())
  relation_name

    Nama dari bidang dimana anda ingin saring hubungan.

  condition

    Sebuah obyek `Q` untuk mengendalikan penyaringan.

`FilteredRelation` is used with `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",
    ... )

karena penyaringan dalam klausa `WHERE` dari himpunan permintaan pertama hanya akan mengoperasikan pada pizza vegetarian.

`FilteredRelation` tidak mendukung:

- `QuerySet.only()` dan `prefetch_related()`.

- Sebuah `GenericForeignKey` diwarisi dari model induk.