• Language: en

PostgreSQL specific model fields

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

ArrayField

class ArrayField(base_field, size=None, **options)[source]

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

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

base_field

This is a required argument.

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).

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

from django.db import models
from django.contrib.postgres.fields import ArrayField

class ChessBoard(models.Model):
    board = ArrayField(
        ArrayField(
            models.CharField(max_length=10, blank=True),
            size=8,
        ),
        size=8,
    )

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

size

This is an optional argument.

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

Note

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],
])

If irregular shapes are required, then the underlying field should be made nullable and the values padded with None.

Querying ArrayField

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

from django.db import models
from django.contrib.postgres.fields import ArrayField

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'])
>>> 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>]>

len

Returns the length of the array. The lookups available afterwards 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

This class of transforms allows you to index into the array in queries. 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 []>

Note

PostgreSQL uses 1-based indexing for array fields when writing raw SQL. However these indexes and those used in slices use 0-based indexing to be consistent with Python.

Slice transforms

This class of transforms allow you to 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>]>

Note

PostgreSQL uses 1-based indexing for array fields when writing raw SQL. However these slices and those used in indexes use 0-based indexing to be consistent with Python.

Multidimensional arrays with indexes and slices

PostgreSQL has some rather esoteric behavior when using indexes and slices on multidimensional arrays. It will always work to use indexes to reach down to the final underlying data, but most other slices behave strangely at the database level and cannot be supported in a logical, consistent fashion by Django.

Indexing ArrayField

At present using db_index will create a btree index. This does not offer particularly significant help to querying. A more useful index is a GIN index, which you should create using a RunSQL operation.

CIText fields

class CIText(**options)[source]
New in Django 1.11.

A mixin to create case-insensitive text fields backed by the citext type. Read about the performance considerations prior to using it.

To use citext, use the CITextExtension operation to setup the citext extension in PostgreSQL before the first CreateModel migration operation.

Several fields that use the mixin are provided:

class CICharField(**options)[source]
class CIEmailField(**options)[source]
class CITextField(**options)[source]

These fields subclass CharField, EmailField, and TextField, respectively.

max_length won’t be enforced in the database since citext behaves similar to PostgreSQL’s text type.

HStoreField

class HStoreField(**options)[source]

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

To use this field, you’ll need to:

  1. Add 'django.contrib.postgres' in your INSTALLED_APPS.
  2. Setup the hstore extension in PostgreSQL.

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

Changed in Django 1.11:

Added the ability to store nulls. Previously, they were cast to strings.

Note

On occasions it may be useful to require or restrict the keys which are valid for a given field. This can be done using the KeysValidator.

Querying HStoreField

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

We will use the following example model:

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

Key lookups

To query based on a given key, you simply 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>]>

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

Warning

Since any string could be a key in a hstore value, any lookup other than those listed below will be interpreted as a key lookup. No errors are raised. Be extra careful for typing mistakes, and always check your queries work as you intend.

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 avalues(). 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>]>

JSONField

class JSONField(encoder=None, **options)[source]

A field for storing JSON encoded data. In Python the data is represented in its Python native format: dictionaries, lists, strings, numbers, booleans and None.

encoder
New in Django 1.11.

An optional JSON-encoding class to serialize data types not supported by the standard JSON serializer (datetime, uuid, etc.). For example, you can use the DjangoJSONEncoder class or any other json.JSONEncoder subclass.

When the value is retrieved from the database, it will be in the format chosen by the custom encoder (most often a string), so you’ll need to take extra steps to convert the value back to the initial data type (Model.from_db() and Field.from_db_value() are two possible hooks for that purpose). Your deserialization may need to account for the fact that you can’t be certain of the input type. For example, you run the risk of returning a datetime that was actually a string that just happened to be in the same format chosen for datetimes.

If you give the field a default, ensure it’s a callable such as dict (for an empty default) or a callable that returns a dict (such as a function). Incorrectly using default={} creates a mutable default that is shared between all instances of JSONField.

Note

PostgreSQL has two native JSON based data types: json and jsonb. The main difference between them is how they are stored and how they can be queried. PostgreSQL’s json field is stored as the original string representation of the JSON and must be decoded on the fly when queried based on keys. The jsonb field is stored based on the actual structure of the JSON which allows indexing. The trade-off is a small additional cost on writing to the jsonb field. JSONField uses jsonb.

As a result, this field requires PostgreSQL ≥ 9.4.

Querying JSONField

We will use the following example model:

from django.contrib.postgres.fields import JSONField
from django.db import models

class Dog(models.Model):
    name = models.CharField(max_length=200)
    data = JSONField()

    def __str__(self):
        return self.name

Key, index, and path lookups

To query based on a given dictionary key, simply use that key as the lookup name:

>>> Dog.objects.create(name='Rufus', data={
...     'breed': 'labrador',
...     'owner': {
...         'name': 'Bob',
...         'other_pets': [{
...             'name': 'Fishy',
...         }],
...     },
... })
>>> Dog.objects.create(name='Meg', data={'breed': 'collie'})

>>> Dog.objects.filter(data__breed='collie')
<QuerySet [<Dog: Meg>]>

Multiple keys can be chained together to form a path lookup:

>>> Dog.objects.filter(data__owner__name='Bob')
<QuerySet [<QuerySet <Dog: Rufus>]>

If the key is an integer, it will be interpreted as an index lookup in an array:

>>> Dog.objects.filter(data__owner__other_pets__0__name='Fishy')
<QuerySet [<Dog: Rufus>]>

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

If only one key or index is used, the SQL operator -> is used. If multiple operators are used then the #> operator is used.

Warning

Since any string could be a key in a JSON object, any lookup other than those listed below will be interpreted as a key lookup. No errors are raised. Be extra careful for typing mistakes, and always check your queries work as you intend.

Containment and key operations

JSONField shares lookups relating to containment and keys with HStoreField.

Range Fields

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

All of the range fields translate to psycopg2 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, [).

IntegerRangeField

class IntegerRangeField(**options)[source]

Stores a range of integers. Based on an IntegerField. Represented by an int4range in the database and a 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

class BigIntegerRangeField(**options)[source]

Stores a range of large integers. Based on a BigIntegerField. Represented by an int8range in the database and a 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 [).

FloatRangeField

class FloatRangeField(**options)[source]

Stores a range of floating point values. Based on a FloatField. Represented by a numrange in the database and a NumericRange in Python.

DateTimeRangeField

class DateTimeRangeField(**options)[source]

Stores a range of timestamps. Based on a DateTimeField. Represented by a tztsrange in the database and a DateTimeTZRange in Python.

DateRangeField

class DateRangeField(**options)[source]

Stores a range of dates. Based on a DateField. Represented by a daterange in the database and a 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 [).

Querying Range Fields

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

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))

and NumericRange:

>>> from psycopg2.extras import NumericRange

Containment functions

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

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: IntegerField, BigIntegerField, FloatField, DateField, and DateTimeField. For example:

>>> from psycopg2.extras 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>]>

Comparison functions

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

fully_lt

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

>>> Event.objects.filter(ages__fully_lt=NumericRange(11, 15))
<QuerySet [<Event: Soft play>]>
fully_gt

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

>>> Event.objects.filter(ages__fully_gt=NumericRange(11, 15))
<QuerySet [<Event: Pub trip>]>
not_lt

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

>>> Event.objects.filter(ages__not_lt=NumericRange(0, 15))
<QuerySet [<Event: Soft play>, <Event: Pub trip>]>
not_gt

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

>>> Event.objects.filter(ages__not_gt=NumericRange(3, 10))
<QuerySet [<Event: Soft play>]>
adjacent_to

The returned ranges share a bound with the passed range.

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

Querying using the bounds

There are three transforms available for use in queries. You can extract the lower or upper bound, or query based on emptiness.

startswith

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

>>> Event.objects.filter(ages__startswith=21)
<QuerySet [<Event: Pub trip>]>
endswith

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

>>> Event.objects.filter(ages__endswith=10)
<QuerySet [<Event: Soft play>]>
isempty

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

>>> Event.objects.filter(ages__isempty=True)
<QuerySet []>

Defining your own range types

PostgreSQL allows the definition of custom range types. Django’s model and form field implementations use base classes below, and psycopg2 provides a register_range() to allow use of custom range types.

class RangeField(**options)[source]

Base class for model range fields.

base_field

The model field class to use.

range_type

The psycopg2 range type to use.

form_field

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

class django.contrib.postgres.forms.BaseRangeField

Base class for form range fields.

base_field

The form field to use.

range_type

The psycopg2 range type to use.

Back to Top