PostgreSQL specific model fields ================================ All of these fields are available from the ``django.contrib.postgres.fields`` module. .. currentmodule:: django.contrib.postgres.fields ArrayField ---------- .. class:: ArrayField(base_field, size=None, **options) A field for storing lists of data. Most field types can be used, you simply pass another field instance as the :attr:`base_field `. You may also specify a :attr:`size `. ``ArrayField`` can be nested to store multi-dimensional arrays. .. attribute:: 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 :class:`~django.db.models.Field`. For example, it could be an :class:`~django.db.models.IntegerField` or a :class:`~django.db.models.CharField`. Most field types are permitted, with the exception of those handling relational data (:class:`~django.db.models.ForeignKey`, :class:`~django.db.models.OneToOneField` and :class:`~django.db.models.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( CharField(max_length=10, blank=True, null=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. .. attribute:: 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 :class:`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 .. fieldlookup:: arrayfield.contains contains ~~~~~~~~ The :lookup:`contains` lookup is overridden on :class:`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']) [, ] >>> Post.objects.filter(tags__contains=['django']) [, ] >>> Post.objects.filter(tags__contains=['django', 'thoughts']) [] .. fieldlookup:: arrayfield.contained_by contained_by ~~~~~~~~~~~~ This is the inverse of the :lookup:`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']) [, ] >>> Post.objects.filter(tags__contained_by=['thoughts', 'django', 'tutorial']) [, , ] .. fieldlookup:: arrayfield.overlap 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']) [, ] >>> Post.objects.filter(tags__overlap=['thoughts', 'tutorial']) [, , ] .. fieldlookup:: arrayfield.len len ~~~ Returns the length of the array. The lookups available afterwards are those available for :class:`~django.db.models.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) [] .. fieldlookup:: arrayfield.index 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 :attr:`size ` of the array. The lookups available after the transform are those from the :attr:`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') [, ] >>> Post.objects.filter(tags__1__iexact='Django') [] >>> Post.objects.filter(tags__276='javascript') [] .. note:: PostgreSQL uses 1-based indexing for array fields when writing raw SQL. However these indexes and those used in :lookup:`slices ` use 0-based indexing to be consistent with Python. .. fieldlookup:: arrayfield.slice 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']) [] >>> Post.objects.filter(tags__0_2__contains='thoughts') [, ] .. note:: PostgreSQL uses 1-based indexing for array fields when writing raw SQL. However these slices and those used in :lookup:`indexes ` use 0-based indexing to be consistent with Python. .. admonition:: 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 :attr:`~django.db.models.Field.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 :class:`~django.db.migrations.operations.RunSQL` operation. HStoreField ----------- .. class:: HStoreField(**options) A field for storing mappings of strings to strings. The Python data type used is a ``dict``. .. 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 :class:`~django.contrib.postgres.validators.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): # __unicode__ on Python 2 return self.name .. fieldlookup:: hstorefield.key 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') [] You can chain other lookups after key lookups:: >>> Dog.objects.filter(data__breed__contains='l') [, ] If the key you wish to query by clashes with the name of another lookup, you need to use the :lookup:`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. .. fieldlookup:: hstorefield.contains contains ~~~~~~~~ The :lookup:`contains` lookup is overridden on :class:`~django.contrib.postgres.fields.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'}) [, ] >>> Dog.objects.filter(data__contains={'breed': 'collie'}) [] .. fieldlookup:: hstorefield.contained_by contained_by ~~~~~~~~~~~~ This is the inverse of the :lookup:`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'}) [, ] >>> Dog.objects.filter(data__contained_by={'breed': 'collie'}) [] .. fieldlookup:: hstorefield.has_key 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') [] .. fieldlookup:: hstorefield.has_keys 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']) [] .. fieldlookup:: hstorefield.keys 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 :class:`~django.contrib.postgres.fields.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']) [, ] .. fieldlookup:: hstorefield.values 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 :class:`~django.contrib.postgres.fields.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']) []