{"title":"PostgreSQL specific model fields","version":"5.2","locale":"ko","docname":"ref/contrib/postgres/fields","url":"/ko/5.2/ref/contrib/postgres/fields/","canonical":"https://djangodocs.dev/ko/5.2/ref/contrib/postgres/fields/","summary":"All of these fields are available from the django.contrib.postgres.fields module. Indexing these fields Link to this heading # Index and Field.db_index both create…","html":"

PostgreSQL specific model fieldsLink to this heading#

\n

All of these fields are available from the django.contrib.postgres.fields\nmodule.

\n
\n

Indexing these fieldsLink to this heading#

\n

Index and Field.db_index both create a\nB-tree index, which isn’t particularly helpful when querying complex data types.\nIndexes such as GinIndex and\nGistIndex are better suited, though\nthe index choice is dependent on the queries that you’re using. Generally, GiST\nmay be a good choice for the range fields and\nHStoreField, and GIN may be helpful for ArrayField.

\n
\n
\n

ArrayFieldLink to this heading#

\n
\n
\nclass ArrayField(base_field, size=None, **options)Link to this definition#
\n

A field for storing lists of data. Most field types can be used, and you\npass another field instance as the base_field. You may also specify a size. ArrayField can be nested to store multi-dimensional\narrays.

\n

If you give the field a default, ensure\nit’s a callable such as list (for an empty default) or a callable that\nreturns a list (such as a function). Incorrectly using default=[]\ncreates a mutable default that is shared between all instances of\nArrayField.

\n
\n
\nbase_fieldLink to this definition#
\n

This is a required argument.

\n

Specifies the underlying data type and behavior for the array. It\nshould be an instance of a subclass of\nField. For example, it could be an\nIntegerField or a\nCharField. Most field types are permitted,\nwith the exception of those handling relational data\n(ForeignKey,\nOneToOneField and\nManyToManyField) and file fields\n(FileField and\nImageField).

\n

It is possible to nest array fields - you can specify an instance of\nArrayField as the base_field. For example:

\n
Code
from django.contrib.postgres.fields import ArrayField\nfrom django.db import models\n\n\nclass ChessBoard(models.Model):\n    board = ArrayField(\n        ArrayField(\n            models.CharField(max_length=10, blank=True),\n            size=8,\n        ),\n        size=8,\n    )\n
\n

Transformation of values between the database and the model, validation\nof data and configuration, and serialization are all delegated to the\nunderlying base field.

\n
\n\n
\n
\nsizeLink to this definition#
\n

This is an optional argument.

\n

If passed, the array will have a maximum size as specified. This will\nbe passed to the database, although PostgreSQL at present does not\nenforce the restriction.

\n
\n\n
\n\n\n
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Querying ArrayFieldLink to this heading#

\n

There are a number of custom lookups and transforms for ArrayField.\nWe will use the following example model:

\n
Code
from django.contrib.postgres.fields import ArrayField\nfrom django.db import models\n\n\nclass Post(models.Model):\n    name = models.CharField(max_length=200)\n    tags = ArrayField(models.CharField(max_length=200), blank=True)\n\n    def __str__(self):\n        return self.name\n
\n
\n

containsLink to this heading#

\n

The contains lookup is overridden on ArrayField. The\nreturned objects will be those where the values passed are a subset of the\ndata. It uses the SQL operator @>. For example:

\n
Python console
>>> Post.objects.create(name="First post", tags=["thoughts", "django"])\n>>> Post.objects.create(name="Second post", tags=["thoughts"])\n>>> Post.objects.create(name="Third post", tags=["tutorial", "django"])\n\n>>> Post.objects.filter(tags__contains=["thoughts"])\n<QuerySet [<Post: First post>, <Post: Second post>]>\n\n>>> Post.objects.filter(tags__contains=["django"])\n<QuerySet [<Post: First post>, <Post: Third post>]>\n\n>>> Post.objects.filter(tags__contains=["django", "thoughts"])\n<QuerySet [<Post: First post>]>\n
\n
\n
\n

contained_byLink to this heading#

\n

This is the inverse of the contains lookup -\nthe objects returned will be those where the data is a subset of the values\npassed. It uses the SQL operator <@. For example:

\n
Python console
>>> Post.objects.create(name="First post", tags=["thoughts", "django"])\n>>> Post.objects.create(name="Second post", tags=["thoughts"])\n>>> Post.objects.create(name="Third post", tags=["tutorial", "django"])\n\n>>> Post.objects.filter(tags__contained_by=["thoughts", "django"])\n<QuerySet [<Post: First post>, <Post: Second post>]>\n\n>>> Post.objects.filter(tags__contained_by=["thoughts", "django", "tutorial"])\n<QuerySet [<Post: First post>, <Post: Second post>, <Post: Third post>]>\n
\n
\n
\n

overlapLink to this heading#

\n

Returns objects where the data shares any results with the values passed. Uses\nthe SQL operator &&. For example:

\n
Python console
>>> Post.objects.create(name="First post", tags=["thoughts", "django"])\n>>> Post.objects.create(name="Second post", tags=["thoughts", "tutorial"])\n>>> Post.objects.create(name="Third post", tags=["tutorial", "django"])\n\n>>> Post.objects.filter(tags__overlap=["thoughts"])\n<QuerySet [<Post: First post>, <Post: Second post>]>\n\n>>> Post.objects.filter(tags__overlap=["thoughts", "tutorial"])\n<QuerySet [<Post: First post>, <Post: Second post>, <Post: Third post>]>\n\n>>> Post.objects.filter(tags__overlap=Post.objects.values_list("tags"))\n<QuerySet [<Post: First post>, <Post: Second post>, <Post: Third post>]>\n
\n
\n
\n

lenLink to this heading#

\n

Returns the length of the array. The lookups available afterward are those\navailable for IntegerField. For example:

\n
Python console
>>> Post.objects.create(name="First post", tags=["thoughts", "django"])\n>>> Post.objects.create(name="Second post", tags=["thoughts"])\n\n>>> Post.objects.filter(tags__len=1)\n<QuerySet [<Post: Second post>]>\n
\n
\n
\n

Index transformsLink to this heading#

\n

Index transforms index into the array. Any non-negative integer can be used.\nThere are no errors if it exceeds the size of the\narray. The lookups available after the transform are those from the\nbase_field. For example:

\n
Python console
>>> Post.objects.create(name="First post", tags=["thoughts", "django"])\n>>> Post.objects.create(name="Second post", tags=["thoughts"])\n\n>>> Post.objects.filter(tags__0="thoughts")\n<QuerySet [<Post: First post>, <Post: Second post>]>\n\n>>> Post.objects.filter(tags__1__iexact="Django")\n<QuerySet [<Post: First post>]>\n\n>>> Post.objects.filter(tags__276="javascript")\n<QuerySet []>\n
\n\n
\n
\n

Slice transformsLink to this heading#

\n

Slice transforms take a slice of the array. Any two non-negative integers can\nbe used, separated by a single underscore. The lookups available after the\ntransform do not change. For example:

\n
Python console
>>> Post.objects.create(name="First post", tags=["thoughts", "django"])\n>>> Post.objects.create(name="Second post", tags=["thoughts"])\n>>> Post.objects.create(name="Third post", tags=["django", "python", "thoughts"])\n\n>>> Post.objects.filter(tags__0_1=["thoughts"])\n<QuerySet [<Post: First post>, <Post: Second post>]>\n\n>>> Post.objects.filter(tags__0_2__contains=["thoughts"])\n<QuerySet [<Post: First post>, <Post: Second post>]>\n
\n\n\n
\n
\n
\n
\n

HStoreFieldLink to this heading#

\n
\n
\nclass HStoreField(**options)Link to this definition#
\n

A field for storing key-value pairs. The Python data type used is a\ndict. Keys must be strings, and values may be either strings or nulls\n(None in Python).

\n

To use this field, you’ll need to:

\n
    \n

  1. Add 'django.contrib.postgres' in your INSTALLED_APPS.

    2. \n

  2. Set up the hstore extension in\nPostgreSQL.

    3. \n
\n

You’ll see an error like can't adapt type 'dict' if you skip the first\nstep, or type "hstore" does not exist if you skip the second.

\n
\n\n\n
\n

Querying HStoreFieldLink to this heading#

\n

In addition to the ability to query by key, there are a number of custom\nlookups available for HStoreField.

\n

We will use the following example model:

\n
Code
from django.contrib.postgres.fields import HStoreField\nfrom django.db import models\n\n\nclass Dog(models.Model):\n    name = models.CharField(max_length=200)\n    data = HStoreField()\n\n    def __str__(self):\n        return self.name\n
\n
\n

Key lookupsLink to this heading#

\n

To query based on a given key, you can use that key as the lookup name:

\n
Python console
>>> Dog.objects.create(name="Rufus", data={"breed": "labrador"})\n>>> Dog.objects.create(name="Meg", data={"breed": "collie"})\n\n>>> Dog.objects.filter(data__breed="collie")\n<QuerySet [<Dog: Meg>]>\n
\n

You can chain other lookups after key lookups:

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Python console
>>> Dog.objects.filter(data__breed__contains="l")\n<QuerySet [<Dog: Rufus>, <Dog: Meg>]>\n
\n

or use F() expressions to annotate a key value. For example:

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Python console
>>> from django.db.models import F\n>>> rufus = Dog.objects.annotate(breed=F("data__breed"))[0]\n>>> rufus.breed\n'labrador'\n
\n

If the key you wish to query by clashes with the name of another lookup, you\nneed to use the hstorefield.contains lookup instead.

\n\n\n
\n
\n

containsLink to this heading#

\n

The contains lookup is overridden on\nHStoreField. The returned objects are\nthose where the given dict of key-value pairs are all contained in the\nfield. It uses the SQL operator @>. For example:

\n
Python console
>>> Dog.objects.create(name="Rufus", data={"breed": "labrador", "owner": "Bob"})\n>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})\n>>> Dog.objects.create(name="Fred", data={})\n\n>>> Dog.objects.filter(data__contains={"owner": "Bob"})\n<QuerySet [<Dog: Rufus>, <Dog: Meg>]>\n\n>>> Dog.objects.filter(data__contains={"breed": "collie"})\n<QuerySet [<Dog: Meg>]>\n
\n
\n
\n

contained_byLink to this heading#

\n

This is the inverse of the contains lookup -\nthe objects returned will be those where the key-value pairs on the object are\na subset of those in the value passed. It uses the SQL operator <@. For\nexample:

\n
Python console
>>> Dog.objects.create(name="Rufus", data={"breed": "labrador", "owner": "Bob"})\n>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})\n>>> Dog.objects.create(name="Fred", data={})\n\n>>> Dog.objects.filter(data__contained_by={"breed": "collie", "owner": "Bob"})\n<QuerySet [<Dog: Meg>, <Dog: Fred>]>\n\n>>> Dog.objects.filter(data__contained_by={"breed": "collie"})\n<QuerySet [<Dog: Fred>]>\n
\n
\n
\n

has_keyLink to this heading#

\n

Returns objects where the given key is in the data. Uses the SQL operator\n?. For example:

\n
Python console
>>> Dog.objects.create(name="Rufus", data={"breed": "labrador"})\n>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})\n\n>>> Dog.objects.filter(data__has_key="owner")\n<QuerySet [<Dog: Meg>]>\n
\n
\n
\n

has_any_keysLink to this heading#

\n

Returns objects where any of the given keys are in the data. Uses the SQL\noperator ?|. For example:

\n
Python console
>>> Dog.objects.create(name="Rufus", data={"breed": "labrador"})\n>>> Dog.objects.create(name="Meg", data={"owner": "Bob"})\n>>> Dog.objects.create(name="Fred", data={})\n\n>>> Dog.objects.filter(data__has_any_keys=["owner", "breed"])\n<QuerySet [<Dog: Rufus>, <Dog: Meg>]>\n
\n
\n
\n

has_keysLink to this heading#

\n

Returns objects where all of the given keys are in the data. Uses the SQL operator\n?&. For example:

\n
Python console
>>> Dog.objects.create(name="Rufus", data={})\n>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})\n\n>>> Dog.objects.filter(data__has_keys=["breed", "owner"])\n<QuerySet [<Dog: Meg>]>\n
\n
\n
\n

keysLink to this heading#

\n

Returns objects where the array of keys is the given value. Note that the order\nis not guaranteed to be reliable, so this transform is mainly useful for using\nin conjunction with lookups on\nArrayField. Uses the SQL function\nakeys(). For example:

\n
Python console
>>> Dog.objects.create(name="Rufus", data={"toy": "bone"})\n>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})\n\n>>> Dog.objects.filter(data__keys__overlap=["breed", "toy"])\n<QuerySet [<Dog: Rufus>, <Dog: Meg>]>\n
\n
\n
\n

valuesLink to this heading#

\n

Returns objects where the array of values is the given value. Note that the\norder is not guaranteed to be reliable, so this transform is mainly useful for\nusing in conjunction with lookups on\nArrayField. Uses the SQL function\navals(). For example:

\n
Python console
>>> Dog.objects.create(name="Rufus", data={"breed": "labrador"})\n>>> Dog.objects.create(name="Meg", data={"breed": "collie", "owner": "Bob"})\n\n>>> Dog.objects.filter(data__values__contains=["collie"])\n<QuerySet [<Dog: Meg>]>\n
\n
\n
\n
\n
\n

Range FieldsLink to this heading#

\n

There are five range field types, corresponding to the built-in range types in\nPostgreSQL. These fields are used to store a range of values; for example the\nstart and end timestamps of an event, or the range of ages an activity is\nsuitable for.

\n

All of the range fields translate to psycopg Range objects in Python, but also accept tuples as input if no bounds\ninformation is necessary. The default is lower bound included, upper bound\nexcluded, that is [) (see the PostgreSQL documentation for details about\ndifferent bounds). The default bounds can be changed for non-discrete range\nfields (DateTimeRangeField and DecimalRangeField) by using\nthe default_bounds argument.

\n\n
\n

IntegerRangeFieldLink to this heading#

\n
\n
\nclass IntegerRangeField(**options)Link to this definition#
\n

Stores a range of integers. Based on an\nIntegerField. Represented by an int4range in\nthe database and a\ndjango.db.backends.postgresql.psycopg_any.NumericRange in Python.

\n

Regardless of the bounds specified when saving the data, PostgreSQL always\nreturns a range in a canonical form that includes the lower bound and\nexcludes the upper bound, that is [).

\n
\n\n
\n
\n

BigIntegerRangeFieldLink to this heading#

\n
\n
\nclass BigIntegerRangeField(**options)Link to this definition#
\n

Stores a range of large integers. Based on a\nBigIntegerField. Represented by an int8range\nin the database and a\ndjango.db.backends.postgresql.psycopg_any.NumericRange in Python.

\n

Regardless of the bounds specified when saving the data, PostgreSQL always\nreturns a range in a canonical form that includes the lower bound and\nexcludes the upper bound, that is [).

\n
\n\n
\n
\n

DecimalRangeFieldLink to this heading#

\n
\n
\nclass DecimalRangeField(default_bounds='[)', **options)Link to this definition#
\n

Stores a range of floating point values. Based on a\nDecimalField. Represented by a numrange in\nthe database and a\ndjango.db.backends.postgresql.psycopg_any.NumericRange in Python.

\n
\n
\ndefault_boundsLink to this definition#
\n

Optional. The value of bounds for list and tuple inputs. The\ndefault is lower bound included, upper bound excluded, that is [)\n(see the PostgreSQL documentation for details about\ndifferent bounds). default_bounds is not used for\ndjango.db.backends.postgresql.psycopg_any.NumericRange inputs.

\n
\n\n
\n\n
\n
\n

DateTimeRangeFieldLink to this heading#

\n
\n
\nclass DateTimeRangeField(default_bounds='[)', **options)Link to this definition#
\n

Stores a range of timestamps. Based on a\nDateTimeField. Represented by a tstzrange in\nthe database and a\ndjango.db.backends.postgresql.psycopg_any.DateTimeTZRange in Python.

\n
\n
\ndefault_boundsLink to this definition#
\n

Optional. The value of bounds for list and tuple inputs. The\ndefault is lower bound included, upper bound excluded, that is [)\n(see the PostgreSQL documentation for details about\ndifferent bounds). default_bounds is not used for\ndjango.db.backends.postgresql.psycopg_any.DateTimeTZRange inputs.

\n
\n\n
\n\n
\n
\n

DateRangeFieldLink to this heading#

\n
\n
\nclass DateRangeField(**options)Link to this definition#
\n

Stores a range of dates. Based on a\nDateField. Represented by a daterange in the\ndatabase and a django.db.backends.postgresql.psycopg_any.DateRange in\nPython.

\n

Regardless of the bounds specified when saving the data, PostgreSQL always\nreturns a range in a canonical form that includes the lower bound and\nexcludes the upper bound, that is [).

\n
\n\n
\n
\n

Querying Range FieldsLink to this heading#

\n

There are a number of custom lookups and transforms for range fields. They are\navailable on all the above fields, but we will use the following example\nmodel:

\n
Code
from django.contrib.postgres.fields import IntegerRangeField\nfrom django.db import models\n\n\nclass Event(models.Model):\n    name = models.CharField(max_length=200)\n    ages = IntegerRangeField()\n    start = models.DateTimeField()\n\n    def __str__(self):\n        return self.name\n
\n

We will also use the following example objects:

\n
Python console
>>> import datetime\n>>> from django.utils import timezone\n>>> now = timezone.now()\n>>> Event.objects.create(name="Soft play", ages=(0, 10), start=now)\n>>> Event.objects.create(\n...     name="Pub trip", ages=(21, None), start=now - datetime.timedelta(days=1)\n... )\n
\n

and NumericRange:

\n
Code
>>> from django.db.backends.postgresql.psycopg_any import NumericRange\n
\n
\n

Containment functionsLink to this heading#

\n

As with other PostgreSQL fields, there are three standard containment\noperators: contains, contained_by and overlap, using the SQL\noperators @>, <@, and && respectively.

\n
\n
containsLink to this heading#
\n
Code
>>> Event.objects.filter(ages__contains=NumericRange(4, 5))\n<QuerySet [<Event: Soft play>]>\n
\n
\n
\n
contained_byLink to this heading#
\n
Code
>>> Event.objects.filter(ages__contained_by=NumericRange(0, 15))\n<QuerySet [<Event: Soft play>]>\n
\n

The contained_by lookup is also available on the non-range field types:\nSmallAutoField,\nAutoField, BigAutoField,\nSmallIntegerField,\nIntegerField,\nBigIntegerField,\nDecimalField, FloatField,\nDateField, and\nDateTimeField. For example:

\n
Python console
>>> from django.db.backends.postgresql.psycopg_any import DateTimeTZRange\n>>> Event.objects.filter(\n...     start__contained_by=DateTimeTZRange(\n...         timezone.now() - datetime.timedelta(hours=1),\n...         timezone.now() + datetime.timedelta(hours=1),\n...     ),\n... )\n<QuerySet [<Event: Soft play>]>\n
\n
\n
\n
overlapLink to this heading#
\n
Code
>>> Event.objects.filter(ages__overlap=NumericRange(8, 12))\n<QuerySet [<Event: Soft play>]>\n
\n
\n
\n
\n

Comparison functionsLink to this heading#

\n

Range fields support the standard lookups: lt, gt,\nlte and gte. These are not particularly helpful - they\ncompare the lower bounds first and then the upper bounds only if necessary.\nThis is also the strategy used to order by a range field. It is better to use\nthe specific range comparison operators.

\n
\n
fully_ltLink to this heading#
\n

The returned ranges are strictly less than the passed range. In other words,\nall the points in the returned range are less than all those in the passed\nrange.

\n
Code
>>> Event.objects.filter(ages__fully_lt=NumericRange(11, 15))\n<QuerySet [<Event: Soft play>]>\n
\n
\n
\n
fully_gtLink to this heading#
\n

The returned ranges are strictly greater than the passed range. In other words,\nthe all the points in the returned range are greater than all those in the\npassed range.

\n
Code
>>> Event.objects.filter(ages__fully_gt=NumericRange(11, 15))\n<QuerySet [<Event: Pub trip>]>\n
\n
\n
\n
not_ltLink to this heading#
\n

The returned ranges do not contain any points less than the passed range, that\nis the lower bound of the returned range is at least the lower bound of the\npassed range.

\n
Code
>>> Event.objects.filter(ages__not_lt=NumericRange(0, 15))\n<QuerySet [<Event: Soft play>, <Event: Pub trip>]>\n
\n
\n
\n
not_gtLink to this heading#
\n

The returned ranges do not contain any points greater than the passed range, that\nis the upper bound of the returned range is at most the upper bound of the\npassed range.

\n
Code
>>> Event.objects.filter(ages__not_gt=NumericRange(3, 10))\n<QuerySet [<Event: Soft play>]>\n
\n
\n
\n
adjacent_toLink to this heading#
\n

The returned ranges share a bound with the passed range.

\n
Code
>>> Event.objects.filter(ages__adjacent_to=NumericRange(10, 21))\n<QuerySet [<Event: Soft play>, <Event: Pub trip>]>\n
\n
\n
\n
\n

Querying using the boundsLink to this heading#

\n

Range fields support several extra lookups.

\n
\n
startswithLink to this heading#
\n

Returned objects have the given lower bound. Can be chained to valid lookups\nfor the base field.

\n
Code
>>> Event.objects.filter(ages__startswith=21)\n<QuerySet [<Event: Pub trip>]>\n
\n
\n
\n
endswithLink to this heading#
\n

Returned objects have the given upper bound. Can be chained to valid lookups\nfor the base field.

\n
Code
>>> Event.objects.filter(ages__endswith=10)\n<QuerySet [<Event: Soft play>]>\n
\n
\n
\n
isemptyLink to this heading#
\n

Returned objects are empty ranges. Can be chained to valid lookups for a\nBooleanField.

\n
Code
>>> Event.objects.filter(ages__isempty=True)\n<QuerySet []>\n
\n
\n
\n
lower_incLink to this heading#
\n

Returns objects that have inclusive or exclusive lower bounds, depending on the\nboolean value passed. Can be chained to valid lookups for a\nBooleanField.

\n
Code
>>> Event.objects.filter(ages__lower_inc=True)\n<QuerySet [<Event: Soft play>, <Event: Pub trip>]>\n
\n
\n
\n
lower_infLink to this heading#
\n

Returns objects that have unbounded (infinite) or bounded lower bound,\ndepending on the boolean value passed. Can be chained to valid lookups for a\nBooleanField.

\n
Code
>>> Event.objects.filter(ages__lower_inf=True)\n<QuerySet []>\n
\n
\n
\n
upper_incLink to this heading#
\n

Returns objects that have inclusive or exclusive upper bounds, depending on the\nboolean value passed. Can be chained to valid lookups for a\nBooleanField.

\n
Code
>>> Event.objects.filter(ages__upper_inc=True)\n<QuerySet []>\n
\n
\n
\n
upper_infLink to this heading#
\n

Returns objects that have unbounded (infinite) or bounded upper bound,\ndepending on the boolean value passed. Can be chained to valid lookups for a\nBooleanField.

\n
Code
>>> Event.objects.filter(ages__upper_inf=True)\n<QuerySet [<Event: Pub trip>]>\n
\n
\n
\n
\n
\n

Defining your own range typesLink to this heading#

\n

PostgreSQL allows the definition of custom range types. Django’s model and form\nfield implementations use base classes below, and psycopg provides a\nregister_range() to allow use of custom\nrange types.

\n
\n
\nclass RangeField(**options)Link to this definition#
\n

Base class for model range fields.

\n
\n
\nbase_fieldLink to this definition#
\n

The model field class to use.

\n
\n\n
\n
\nrange_typeLink to this definition#
\n

The range type to use.

\n
\n\n
\n
\nform_fieldLink to this definition#
\n

The form field class to use. Should be a subclass of\ndjango.contrib.postgres.forms.BaseRangeField.

\n
\n\n
\n\n
\n
\nclass django.contrib.postgres.forms.BaseRangeFieldLink to this definition#
\n

Base class for form range fields.

\n
\n
\nbase_fieldLink to this definition#
\n

The form field to use.

\n
\n\n
\n
\nrange_typeLink to this definition#
\n

The range type to use.

\n
\n\n
\n\n
\n
\n

Range operatorsLink to this heading#

\n
\n
\nclass RangeOperatorsLink to this definition#
\n
\n\n

PostgreSQL provides a set of SQL operators that can be used together with the\nrange data types (see the PostgreSQL documentation for the full details of\nrange operators). This class is meant as a\nconvenient method to avoid typos. The operator names overlap with the names of\ncorresponding lookups.

\n
Python
class RangeOperators:\n    EQUAL = "="\n    NOT_EQUAL = "<>"\n    CONTAINS = "@>"\n    CONTAINED_BY = "<@"\n    OVERLAPS = "&&"\n    FULLY_LT = "<<"\n    FULLY_GT = ">>"\n    NOT_LT = "&>"\n    NOT_GT = "&<"\n    ADJACENT_TO = "-|-"\n
\n
\n
\n

RangeBoundary() expressionsLink to this heading#

\n
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\nclass RangeBoundary(inclusive_lower=True, inclusive_upper=False)Link to this definition#
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\ninclusive_lowerLink to this definition#
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If True (default), the lower bound is inclusive '[', otherwise\nit’s exclusive '('.

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\ninclusive_upperLink to this definition#
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If False (default), the upper bound is exclusive ')', otherwise\nit’s inclusive ']'.

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A RangeBoundary() expression represents the range boundaries. It can be\nused with a custom range functions that expected boundaries, for example to\ndefine ExclusionConstraint. See\nthe PostgreSQL documentation for the full details.

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