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. An expression may be a simple value, a reference to a field on the model (or any related models), or an aggregate expression (averages, sums, etc.) that has been computed over the objects that are related to the objects in the QuerySet.

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

The aggregation functions that are provided by Django are described in Aggregation Functions 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.

Waspadalah ketika mengurutkan berdasarkan bidang-bidang dalam model terkait jika anda juga menggunakan distinct(). Lihat catatan dalam distinct() untuk sebuah penjelasan dari bagaimana pengurutan model terkait dapat merubah hasil diharapkan.

class Event(Model):
    parent = models.ForeignKey(
        "self",
        on_delete=models.CASCADE,
        related_name="children",
    )
    date = models.DateField()


Event.objects.order_by("children__date")

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())

Jika anda tidak ingin pengurutan apapun diberlakukan pada permintaan, bahkan tidak pada pengurutan awalan, panggil order_by() tanpa parameter.

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")

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().

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.

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")
[...]

Entry.objects.order_by("blog").distinct("blog")

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'}, ...]>

values_list()¶

values_list(*fields, flat=False, named=False)¶

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

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

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

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

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

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,)]>

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, is_dst=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.

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

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 QuerySets. 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 QuerySets of other models. Passing different models works as long as the SELECT list is the same in all QuerySets (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 QuerySets. 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 QuerySets. 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)

… kemudian sebuah panggilan pada Book.objects.select_related('author__hometown').get(id=4) akan menyimpan sementara Person terkait dan City terkait:

# 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, it must be restricted to a homogeneous set of results. For example, prefetching objects referenced by a GenericForeignKey is only supported if the query is restricted to one ContentType.

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.

Yaitu, semua taburan berhubungan akan diambil dalam sebuah permintaan tunggal, dan digunakan untuk membuat QuerySets yang mempunyai cache pra-diisi dari hasil terkait; QuerySet ini kemudian digunakan dalam panggilan self.toppings.all().

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

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 QuerySets, which normally try to avoid loading all objects into memory before they are needed, even after a query has been executed in the database.

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

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!

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()

Pengambilan penyesuaian juga bekerja dengan hubungan terkait tunggal seperti menyampaikan ForeignKey atau OneToOneField. Umumnya anda akan ingin menggunakan select_related() untuk hubungan ini, tetapi ada sejumlah kasus dimana pengambilan dengan QuerySet penyesuaian berguna:

• 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")

>>> prefetch_related('pizzas__toppings', 'pizzas')

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

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

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.

>>> qs.extra(
...     select={"val": "select col from sometable where othercol = %s"},
...     select_params=(someparam,),
... )

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

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

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

  Argumen select membiarkan anda menaruh bidang tambahan dalam klausa SELECT. Itu harus berupa nama-nama atribut pemetaan sebuah dictionary pada SQL untuk digunakan menghitung atribut itu.

  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. Also note that some database backends, such as some MySQL versions, don’t support subqueries.

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

  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"])
  

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).

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)

Beberapa bidang dalam sebuah model tidak aakan ditunda, bahkan jika anda meminta mereka. Anda tidak pernah dapat menunda memuat primary key. Jika anda sedang menggunakan select_related() mengambil model terkait, anda tidak harus memunda dari bidang yang terhubung ke model utama ke satu terkait, melakukannya akan menghasilkan dalam sebuah kesalahan.

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.

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")

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.

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.

Restaurant.objects.select_for_update(of=("self", "place_ptr"))

On PostgreSQL only, you can pass no_key=True in order to acquire a weaker lock, that still allows creating rows that merely reference locked rows (through a foreign key, for example) while the lock is in place. The PostgreSQL documentation has more details about row-level lock modes.

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 8.0.1+ 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.

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.

AND (&)¶

Combines two QuerySets 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 QuerySets using the SQL XOR operator.

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

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

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()

Sebuah metode nyaman untuk membuat sebuah obyek dan menyimpan itu semua dalam satu langkah. Jadi:

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)},
)

Katakunci argumen apapun dilewatkan ke get_or_create() — kecuali sebuah pilihan dipanggil defaults — akan digunakan dalam panggilan get(). Jika sebuah obyek ditemukan, get_or_create() mengembalikan sebuah tuple dari obyek itu dan False.

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.

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)

>>> 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

update_or_create()¶

update_or_create(defaults=None, **kwargs)¶

aupdate_or_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. The defaults is a dictionary of (field, value) pairs used to update the object. The values in defaults can be callables.

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

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

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

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

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, the primary key attribute can only be retrieved on certain databases (currently PostgreSQL, MariaDB 10.5+, 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 all objects in one batch, except for SQLite where the default is such that at most 999 variables per query are used.

On databases that support it (all but Oracle), setting the ignore_conflicts parameter to True tells the database to ignore failure to insert any rows that fail constraints such as duplicate unique values.

On databases that support it (all except Oracle and SQLite < 3.24), 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 or update_conflicts parameter disable setting the primary key on each model instance (if the database normally support it).

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.
• 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.

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.

Seperti get(), earliest() dan latest() memunculkan DoesNotExist jika tidak ada obyek dengan parameter yang diberikan.

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

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()