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
.
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): # __unicode__ on Python 2 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>]>
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.
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.
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
fieldsclass CIText(**options)
[source]
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:
'django.contrib.postgres'
in your INSTALLED_APPS
.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.
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
.
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): # __unicode__ on Python 2 return self.name
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
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 datetime
s.
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.
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): # __unicode__ on Python 2 return self.name
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 [<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.
JSONField
shares lookups relating to containment and keys with HStoreField
.
contains
(accepts any JSON rather than just a dictionary of strings)contained_by
(accepts any JSON rather than just a dictionary of strings)has_key
has_any_keys
has_keys
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 tstzrange
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 [)
.
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): # __unicode__ on Python 2 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
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>]>
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>]>
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 []>
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.
© Django Software Foundation and individual contributors
Licensed under the BSD License.
https://docs.djangoproject.com/en/1.11/ref/contrib/postgres/fields/