Semua dari bidang ini tersedia dari modul django.contrib.postgres.fields
.
Index
and Field.db_index
both create a
B-tree index, which isn't particularly helpful when querying complex data types.
Indexes such as GinIndex
and
GistIndex
are better suited, though
the index choice is dependent on the queries that you're using. Generally, GiST
may be a good choice for the range fields and
HStoreField
, and GIN may be helpful for ArrayField
.
ArrayField
¶ArrayField
(base_field, size=None, **options)¶A field for storing lists of data. Most field types can be used, and you
pass another field instance as the base_field
. You may also specify a size
. ArrayField
can be nested to store multi-dimensional
arrays.
Jika anda memberikan bidang default
, pastikan itu adalah callable seperti list
(untuk sebuah nilai kosong) atau sebuah callable yang mengembalikan list (seperti sebuah fungsi). Salah menggunakan default=[]
membuat awalan yang berubah-ubah yaitu dibagi diantara semua contoh dari ArrayField
.
base_field
¶Ini adalah sebuah argumen diwajibkan.
Specifies the underlying data type and behavior for the array. It
should be an instance of a subclass of
Field
. For example, it could be an
IntegerField
or a
CharField
. Most field types are permitted,
with the exception of those handling relational data
(ForeignKey
,
OneToOneField
and
ManyToManyField
) and file fields (
FileField
and
ImageField
).
Itu memungkinkan menyarang bidang-bidang larik - anda dapat menentukan sebuah instance dari ArrayField
sebagai base_field
. Sebagai contoh:
from django.contrib.postgres.fields import ArrayField
from django.db import models
class ChessBoard(models.Model):
board = ArrayField(
ArrayField(
models.CharField(max_length=10, blank=True),
size=8,
),
size=8,
)
Perubahan dari nilai-nilai diantara basisdata dan model, pengesahan dari data dan konfigurasi, dan serialisasi adalah semua dilimpahkan ke bidang dasar pokok.
size
¶Ini adalah sebuah argumen pilihan.
Jika dilewatkan, larik akan memiliki ukuran maksimal seperti ditentukan. Ini akan dilewatkan ke basisdata meskipun PostgreSQL saat sekarang tidak melaksanakan batasan.
Catatan
When nesting ArrayField
, whether you use the size
parameter or not,
PostgreSQL requires that the arrays are rectangular:
from django.contrib.postgres.fields import ArrayField
from django.db import models
class Board(models.Model):
pieces = ArrayField(ArrayField(models.IntegerField()))
# Valid
Board(
pieces=[
[2, 3],
[2, 1],
]
)
# Not valid
Board(
pieces=[
[2, 3],
[2],
]
)
Jika bentuk-bentuk tidak beraturan, kemudian bidang pokok harus dibuat null dan nilai-nilai ditambah dengan None
.
ArrayField
¶Ada sejumlah pencarian penyesuaian dan merubah untuk ArrayField
. Kami akan menggunakan model contoh berikut:
from django.contrib.postgres.fields import ArrayField
from django.db import models
class Post(models.Model):
name = models.CharField(max_length=200)
tags = ArrayField(models.CharField(max_length=200), blank=True)
def __str__(self):
return self.name
contains
¶The contains
lookup is overridden on ArrayField
. The
returned objects will be those where the values passed are a subset of the
data. It uses the SQL operator @>
. For example:
>>> Post.objects.create(name="First post", tags=["thoughts", "django"])
>>> Post.objects.create(name="Second post", tags=["thoughts"])
>>> Post.objects.create(name="Third post", tags=["tutorial", "django"])
>>> Post.objects.filter(tags__contains=["thoughts"])
<QuerySet [<Post: First post>, <Post: Second post>]>
>>> Post.objects.filter(tags__contains=["django"])
<QuerySet [<Post: First post>, <Post: Third post>]>
>>> Post.objects.filter(tags__contains=["django", "thoughts"])
<QuerySet [<Post: First post>]>
contained_by
¶This is the inverse of the contains
lookup -
the objects returned will be those where the data is a subset of the values
passed. It uses the SQL operator <@
. For example:
>>> Post.objects.create(name="First post", tags=["thoughts", "django"])
>>> Post.objects.create(name="Second post", tags=["thoughts"])
>>> Post.objects.create(name="Third post", tags=["tutorial", "django"])
>>> Post.objects.filter(tags__contained_by=["thoughts", "django"])
<QuerySet [<Post: First post>, <Post: Second post>]>
>>> Post.objects.filter(tags__contained_by=["thoughts", "django", "tutorial"])
<QuerySet [<Post: First post>, <Post: Second post>, <Post: Third post>]>
overlap
¶Returns objects where the data shares any results with the values passed. Uses
the SQL operator &&
. For example:
>>> Post.objects.create(name="First post", tags=["thoughts", "django"])
>>> Post.objects.create(name="Second post", tags=["thoughts", "tutorial"])
>>> Post.objects.create(name="Third post", tags=["tutorial", "django"])
>>> Post.objects.filter(tags__overlap=["thoughts"])
<QuerySet [<Post: First post>, <Post: Second post>]>
>>> Post.objects.filter(tags__overlap=["thoughts", "tutorial"])
<QuerySet [<Post: First post>, <Post: Second post>, <Post: Third post>]>
>>> Post.objects.filter(tags__overlap=Post.objects.values_list("tags"))
<QuerySet [<Post: First post>, <Post: Second post>, <Post: Third post>]>
Support for QuerySet.values()
and values_list()
as a right-hand
side was added.
len
¶Returns the length of the array. The lookups available afterward are those
available for IntegerField
. For example:
>>> Post.objects.create(name="First post", tags=["thoughts", "django"])
>>> Post.objects.create(name="Second post", tags=["thoughts"])
>>> Post.objects.filter(tags__len=1)
<QuerySet [<Post: Second post>]>
Index transforms index into the array. Any non-negative integer can be used.
There are no errors if it exceeds the size
of the
array. The lookups available after the transform are those from the
base_field
. For example:
>>> Post.objects.create(name="First post", tags=["thoughts", "django"])
>>> Post.objects.create(name="Second post", tags=["thoughts"])
>>> Post.objects.filter(tags__0="thoughts")
<QuerySet [<Post: First post>, <Post: Second post>]>
>>> Post.objects.filter(tags__1__iexact="Django")
<QuerySet [<Post: First post>]>
>>> Post.objects.filter(tags__276="javascript")
<QuerySet []>
Catatan
PostgreSQL menggunakan pengindeksan berdasarkan-1 untuk bidang larik ketika menulis SQL mentah. bagaimanapun indeks-indeks ini dan mereka digunakan dalam slices
menggunakan pengindeksan berdasarkan-0 untuk tetap dengan Python.
Slice transforms take a slice of the array. Any two non-negative integers can be used, separated by a single underscore. The lookups available after the transform do not change. For example:
>>> Post.objects.create(name="First post", tags=["thoughts", "django"])
>>> Post.objects.create(name="Second post", tags=["thoughts"])
>>> Post.objects.create(name="Third post", tags=["django", "python", "thoughts"])
>>> Post.objects.filter(tags__0_1=["thoughts"])
<QuerySet [<Post: First post>, <Post: Second post>]>
>>> Post.objects.filter(tags__0_2__contains=["thoughts"])
<QuerySet [<Post: First post>, <Post: Second post>]>
Catatan
PostgreSQL menggunakan pengindeksan berdasarkan-1 untuk bidang larik ketika menulis SQL mentah. bagaimanapun potongan-potongan ini dan itu yang digunakan dalam indexes
menggunakan pengindeksan berdasarkan-0 untuk tetap dengan Python.
Larik dimensi banyak dengan indeks dan potongan
PostgreSQL mempunyai beberapa perilaku esotorik ketika menggunakan pengindeksan dan pemotongan pada larik banyak dimensi. itu akan selalu bekerja mencapat ke data pokok akhir, tetapi kebanyakan potongan berperilaku aneh pada tingkat basisdata dan tidak dapat didukung dalam logika, gaya tetap oleh Django.
CIText
¶CIText
(**options)¶Ditinggalkan sejak versi 4.2.
Sebuah percampuran untuk membuat bidang-bidang teks kasus-tidak-peka didukung oleh citext type. Baca mengenai the performance considerations sebelum menggunakan itu.
To use citext
, use the CITextExtension
operation to
set up the citext extension in
PostgreSQL before the first CreateModel
migration operation.
Jika anda sedang menggunakan sebuah ArrayField
dari bidang CIText
, anda harus menambah 'django.contrib.postgres'
dalam INSTALLED_APPS
anda, sebaliknya nilai bidang akan muncul sebagai string seperti '{thoughts,django}'
.
Beberapa bidang yang menggunakan mixin disediakan:
CICharField
(**options)¶Ditinggalkan sejak versi 4.2: CICharField
is deprecated in favor of
CharField(db_collation="…")
with a case-insensitive
non-deterministic collation.
CIEmailField
(**options)¶Ditinggalkan sejak versi 4.2: CIEmailField
is deprecated in favor of
EmailField(db_collation="…")
with a case-insensitive
non-deterministic collation.
CITextField
(**options)¶Ditinggalkan sejak versi 4.2: CITextField
is deprecated in favor of
TextField(db_collation="…")
with a case-insensitive
non-deterministic collation.
Bidang ini subkelas CharField
, EmailField
, dan TextField
, masing-masing.
max_length
tidak akan dipaksa dalam basisdata sejak perilaku citext
mirip pada teks text
PostgreSQL.
Case-insensitive collations
It's preferable to use non-deterministic collations instead of the
citext
extension. You can create them using the
CreateCollation
migration
operation. For more details, see Managing collations using migrations and
the PostgreSQL documentation about non-deterministic collations.
HStoreField
¶HStoreField
(**options)¶Sebuah bidang untuk menyimpan pasangan nilai-kunci. Jenis data Python adalah sebuah dict
. Kunci-kunci harus berupa string, dan nilai-nilai mungkin salah satu string atau null (None
dalam Python).
Untuk menggunakan bidang ini, anda akan butuh untuk:
'django.contrib.postgres'
dalam INSTALLED_APPS
anda.Anda akan melihat sebuah kesalahan seperti can't adapt type 'dict'
jika anda melewati langkah pertama, atau type "hstore" does not exist
jika anda melewati kedua.
Catatan
Pada kesempatan itu mungkin berguna untuk membutuhkan atau membatasi kunci-kunci yang sah untuk bidang diberikan. Ini dapat dilakukan menggunakan KeysValidator
.
HStoreField
¶Sebagai tambahan pada kemampuan untuk pencarian berdasarkan kunci, ada angka dari pencarian penyesuaian tersedia untuk HStoreField
.
Kami akan menggunakan model contoh berikut:
from django.contrib.postgres.fields import HStoreField
from django.db import models
class Dog(models.Model):
name = models.CharField(max_length=200)
data = HStoreField()
def __str__(self):
return self.name
To query based on a given key, you can use that key as the lookup name:
>>> Dog.objects.create(name="Rufus", data={"breed": "labrador"})
>>> Dog.objects.create(name="Meg", data={"breed": "collie"})
>>> Dog.objects.filter(data__breed="collie")
<QuerySet [<Dog: Meg>]>
You can chain other lookups after key lookups:
>>> Dog.objects.filter(data__breed__contains="l")
<QuerySet [<Dog: Rufus>, <Dog: Meg>]>
or use F()
expressions to annotate a key value. For example:
>>> from django.db.models import F
>>> rufus = Dog.objects.annotate(breed=F("data__breed"))[0]
>>> rufus.breed
'labrador'
Jika kunci yang anda ahrapkan untuk meminta berdasarkan ketidakcocokan dengan nama dari pencarian lain, anda butuh menggunakan pencarian hstorefield.contains
lookup sebagai gantinya.
Catatan
Key transforms can also be chained with: contains
,
icontains
, endswith
, iendswith
,
iexact
, regex
, iregex
, startswith
,
and istartswith
lookups.
Peringatan
Sejak string apapun dapat berupa sebuah kunci dalam nilai hstore, pencarian apapun dari pada tersebut didaftar dibawah akan diartikan sebagai sebuah pencarian kunci. Tidak ada kesalahan akan dimunculkan. Ekstra hati-hati untuk menulis kesalahan, dan selalu memeriksa permintaan anda bekerja sesuai maksud anda.
contains
¶The contains
lookup is overridden on
HStoreField
. The returned objects are
those where the given dict
of key-value pairs are all contained in the
field. It uses the SQL operator @>
. For example:
>>> Dog.objects.create(name="Rufus", data={"breed": "labrador", "owner": "Bob"})
>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})
>>> Dog.objects.create(name="Fred", data={})
>>> Dog.objects.filter(data__contains={"owner": "Bob"})
<QuerySet [<Dog: Rufus>, <Dog: Meg>]>
>>> Dog.objects.filter(data__contains={"breed": "collie"})
<QuerySet [<Dog: Meg>]>
contained_by
¶This is the inverse of the contains
lookup -
the objects returned will be those where the key-value pairs on the object are
a subset of those in the value passed. It uses the SQL operator <@
. For
example:
>>> Dog.objects.create(name="Rufus", data={"breed": "labrador", "owner": "Bob"})
>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})
>>> Dog.objects.create(name="Fred", data={})
>>> Dog.objects.filter(data__contained_by={"breed": "collie", "owner": "Bob"})
<QuerySet [<Dog: Meg>, <Dog: Fred>]>
>>> Dog.objects.filter(data__contained_by={"breed": "collie"})
<QuerySet [<Dog: Fred>]>
has_key
¶Returns objects where the given key is in the data. Uses the SQL operator
?
. For example:
>>> Dog.objects.create(name="Rufus", data={"breed": "labrador"})
>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})
>>> Dog.objects.filter(data__has_key="owner")
<QuerySet [<Dog: Meg>]>
has_any_keys
¶Returns objects where any of the given keys are in the data. Uses the SQL
operator ?|
. For example:
>>> Dog.objects.create(name="Rufus", data={"breed": "labrador"})
>>> Dog.objects.create(name="Meg", data={"owner": "Bob"})
>>> Dog.objects.create(name="Fred", data={})
>>> Dog.objects.filter(data__has_any_keys=["owner", "breed"])
<QuerySet [<Dog: Rufus>, <Dog: Meg>]>
has_keys
¶Returns objects where all of the given keys are in the data. Uses the SQL operator
?&
. For example:
>>> Dog.objects.create(name="Rufus", data={})
>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})
>>> Dog.objects.filter(data__has_keys=["breed", "owner"])
<QuerySet [<Dog: Meg>]>
keys
¶Returns objects where the array of keys is the given value. Note that the order
is not guaranteed to be reliable, so this transform is mainly useful for using
in conjunction with lookups on
ArrayField
. Uses the SQL function
akeys()
. For example:
>>> Dog.objects.create(name="Rufus", data={"toy": "bone"})
>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})
>>> Dog.objects.filter(data__keys__overlap=["breed", "toy"])
<QuerySet [<Dog: Rufus>, <Dog: Meg>]>
values
¶Returns objects where the array of values is the given value. Note that the
order is not guaranteed to be reliable, so this transform is mainly useful for
using in conjunction with lookups on
ArrayField
. Uses the SQL function
avals()
. For example:
>>> Dog.objects.create(name="Rufus", data={"breed": "labrador"})
>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})
>>> Dog.objects.filter(data__values__contains=["collie"])
<QuerySet [<Dog: Meg>]>
Ada lima jenis jangkauan bidang, berhubungan ke jenis jangkauan siap-pakai dalam PostgreSQL. Bidang-bidang ini digunakan untuk menyimpan jangkauan dari nilai; sebagai contoh stempel waktu awal dan akhir dari sebuah acara, atau jangkauan dari umur sebuah aktivitas yang cocok.
All of the range fields translate to psycopg Range objects in Python, but also accept tuples as input if no bounds
information is necessary. The default is lower bound included, upper bound
excluded, that is [)
(see the PostgreSQL documentation for details about
different bounds). The default bounds can be changed for non-discrete range
fields (DateTimeRangeField
and DecimalRangeField
) by using
the default_bounds
argument.
IntegerRangeField
¶IntegerRangeField
(**options)¶Stores a range of integers. Based on an
IntegerField
. Represented by an int4range
in
the database and a
django.db.backends.postgresql.psycopg_any.NumericRange
in Python.
Regardless of the bounds specified when saving the data, PostgreSQL always
returns a range in a canonical form that includes the lower bound and
excludes the upper bound, that is [)
.
BigIntegerRangeField
¶BigIntegerRangeField
(**options)¶Stores a range of large integers. Based on a
BigIntegerField
. Represented by an int8range
in the database and a
django.db.backends.postgresql.psycopg_any.NumericRange
in Python.
Regardless of the bounds specified when saving the data, PostgreSQL always
returns a range in a canonical form that includes the lower bound and
excludes the upper bound, that is [)
.
DecimalRangeField
¶DecimalRangeField
(default_bounds='[)', **options)¶Stores a range of floating point values. Based on a
DecimalField
. Represented by a numrange
in
the database and a
django.db.backends.postgresql.psycopg_any.NumericRange
in Python.
default_bounds
¶Optional. The value of bounds
for list and tuple inputs. The
default is lower bound included, upper bound excluded, that is [)
(see the PostgreSQL documentation for details about
different bounds). default_bounds
is not used for
django.db.backends.postgresql.psycopg_any.NumericRange
inputs.
DateTimeRangeField
¶DateTimeRangeField
(default_bounds='[)', **options)¶Stores a range of timestamps. Based on a
DateTimeField
. Represented by a tstzrange
in
the database and a
django.db.backends.postgresql.psycopg_any.DateTimeTZRange
in Python.
default_bounds
¶Optional. The value of bounds
for list and tuple inputs. The
default is lower bound included, upper bound excluded, that is [)
(see the PostgreSQL documentation for details about
different bounds). default_bounds
is not used for
django.db.backends.postgresql.psycopg_any.DateTimeTZRange
inputs.
DateRangeField
¶DateRangeField
(**options)¶Stores a range of dates. Based on a
DateField
. Represented by a daterange
in the
database and a django.db.backends.postgresql.psycopg_any.DateRange
in
Python.
Regardless of the bounds specified when saving the data, PostgreSQL always
returns a range in a canonical form that includes the lower bound and
excludes the upper bound, that is [)
.
Ada sejumlah pencarian penyesuaian dan perubahan untuk bidang jangkauan. Mereka tersedia pada semua bidang-bidang diatas, tetapi kami akan menggunakan model contoh berikut:
from django.contrib.postgres.fields import IntegerRangeField
from django.db import models
class Event(models.Model):
name = models.CharField(max_length=200)
ages = IntegerRangeField()
start = models.DateTimeField()
def __str__(self):
return self.name
We will also use the following example objects:
>>> import datetime
>>> from django.utils import timezone
>>> now = timezone.now()
>>> Event.objects.create(name="Soft play", ages=(0, 10), start=now)
>>> Event.objects.create(
... name="Pub trip", ages=(21, None), start=now - datetime.timedelta(days=1)
... )
dan NumericRange
:
>>> from django.db.backends.postgresql.psycopg_any import NumericRange
Seperti bidang-bidang PostgreSQL lainnya, ada tiga standar penahanan penghubung: contains
, contained_by
dan overlap
, menggunakan penghubung SQL @>
, <@
, dan &&
masing-masing.
contains
¶>>> Event.objects.filter(ages__contains=NumericRange(4, 5))
<QuerySet [<Event: Soft play>]>
contained_by
¶>>> Event.objects.filter(ages__contained_by=NumericRange(0, 15))
<QuerySet [<Event: Soft play>]>
The contained_by
lookup is also available on the non-range field types:
SmallAutoField
,
AutoField
, BigAutoField
,
SmallIntegerField
,
IntegerField
,
BigIntegerField
,
DecimalField
, FloatField
,
DateField
, and
DateTimeField
. For example:
>>> from django.db.backends.postgresql.psycopg_any import DateTimeTZRange
>>> Event.objects.filter(
... start__contained_by=DateTimeTZRange(
... timezone.now() - datetime.timedelta(hours=1),
... timezone.now() + datetime.timedelta(hours=1),
... ),
... )
<QuerySet [<Event: Soft play>]>
overlap
¶>>> Event.objects.filter(ages__overlap=NumericRange(8, 12))
<QuerySet [<Event: Soft play>]>
Bidang jangkauan mendukung pencarian standar: lt
, gt
, lte
dan gte
. Ini tidak terlalu membantu - mereka membandingkan batasan terendah dahulu dan batasan tertinggi hanya jika dibutuhkan. Ini juga strategi digunakan untuk mengurutkan berdasarkan bidang jangkauan. Itu lebih baik menggunakan penghubung perbandingan jangkauan khusus.
fully_lt
¶Jangkauan dikembalikan adalah sangat kurang dari jangkauan dilewatkan. Dengan kata lain, semua titik dalam jangkauan dikembalikan kurang dari semua dalam jangkauan dilewatkan.
>>> Event.objects.filter(ages__fully_lt=NumericRange(11, 15))
<QuerySet [<Event: Soft play>]>
fully_gt
¶Jangkauan dikembalikan adalah lebih besar dari jangkauan dilewatkan. Dengan kata lain, semua titik dalam jangkauan dikembalikan lebih besar dari semua dalam jangkauan dilewatkan.
>>> Event.objects.filter(ages__fully_gt=NumericRange(11, 15))
<QuerySet [<Event: Pub trip>]>
not_lt
¶Jangkauan dikembalikan tidak mengandung titik apapun kurang dari jangkauan dilewatkan, yaitu batasan terendah dari jangkauan dikembalikan adalah setidaknya batasan terendah dari jangkauan dilewatkan.
>>> Event.objects.filter(ages__not_lt=NumericRange(0, 15))
<QuerySet [<Event: Soft play>, <Event: Pub trip>]>
not_gt
¶Jangkauan dikembalikan tidak mengandung titik apapun lebih besar dari jangkauan dilewatkan, yaitu batasan tertinggi dari jangkauan dikembalikan adalah batasan paling tertinggi dari jangkauan dilewatkan.
>>> Event.objects.filter(ages__not_gt=NumericRange(3, 10))
<QuerySet [<Event: Soft play>]>
adjacent_to
¶Jangkauan dikembalikan berbagi sebuah batasan dengan jangkauan dilewatkan.
>>> Event.objects.filter(ages__adjacent_to=NumericRange(10, 21))
<QuerySet [<Event: Soft play>, <Event: Pub trip>]>
Range fields support several extra lookups.
startswith
¶Obyek-obyek dikembalikan memiliki batasan terendah diberikan. Dapat diikat untuk pencarian sah untuk bidang dasar.
>>> Event.objects.filter(ages__startswith=21)
<QuerySet [<Event: Pub trip>]>
endswith
¶Obyek-obyek dikembalikan memiliki batasan tertinggi diberikan. Dapat diikat untuk pencarian sah untuk bidang dasar.
>>> Event.objects.filter(ages__endswith=10)
<QuerySet [<Event: Soft play>]>
isempty
¶Obyek-obyek dikembalikan adalah jangkauan kosong. Dapat diikat untuk pencarian sah untuk BooleanField
.
>>> Event.objects.filter(ages__isempty=True)
<QuerySet []>
lower_inc
¶Returns objects that have inclusive or exclusive lower bounds, depending on the
boolean value passed. Can be chained to valid lookups for a
BooleanField
.
>>> Event.objects.filter(ages__lower_inc=True)
<QuerySet [<Event: Soft play>, <Event: Pub trip>]>
lower_inf
¶Returns objects that have unbounded (infinite) or bounded lower bound,
depending on the boolean value passed. Can be chained to valid lookups for a
BooleanField
.
>>> Event.objects.filter(ages__lower_inf=True)
<QuerySet []>
upper_inc
¶Returns objects that have inclusive or exclusive upper bounds, depending on the
boolean value passed. Can be chained to valid lookups for a
BooleanField
.
>>> Event.objects.filter(ages__upper_inc=True)
<QuerySet []>
upper_inf
¶Returns objects that have unbounded (infinite) or bounded upper bound,
depending on the boolean value passed. Can be chained to valid lookups for a
BooleanField
.
>>> Event.objects.filter(ages__upper_inf=True)
<QuerySet [<Event: Pub trip>]>
PostgreSQL allows the definition of custom range types. Django's model and form
field implementations use base classes below, and psycopg
provides a
register_range()
to allow use of custom
range types.
RangeField
(**options)¶Kelas dasar untuk bidang jangkauan model.
base_field
¶Kelas bidang model digunakan.
range_type
¶The range type to use.
form_field
¶Kelas bidang formulir digunakan. Harus berupa subkelas dari django.contrib.postgres.forms.BaseRangeField
.
RangeOperators
¶PostgreSQL provides a set of SQL operators that can be used together with the range data types (see the PostgreSQL documentation for the full details of range operators). This class is meant as a convenient method to avoid typos. The operator names overlap with the names of corresponding lookups.
class RangeOperators:
EQUAL = "="
NOT_EQUAL = "<>"
CONTAINS = "@>"
CONTAINED_BY = "<@"
OVERLAPS = "&&"
FULLY_LT = "<<"
FULLY_GT = ">>"
NOT_LT = "&>"
NOT_GT = "&<"
ADJACENT_TO = "-|-"
RangeBoundary
(inclusive_lower=True, inclusive_upper=False)¶inclusive_lower
¶If True
(default), the lower bound is inclusive '['
, otherwise
it's exclusive '('
.
inclusive_upper
¶If False
(default), the upper bound is exclusive ')'
, otherwise
it's inclusive ']'
.
A RangeBoundary()
expression represents the range boundaries. It can be
used with a custom range functions that expected boundaries, for example to
define ExclusionConstraint
. See
the PostgreSQL documentation for the full details.
Des 04, 2023