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405 lines
13 KiB
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405 lines
13 KiB
Plaintext
PostgreSQL specific model fields
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================================
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All of these fields are available from the ``django.contrib.postgres.fields``
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module.
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.. currentmodule:: django.contrib.postgres.fields
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ArrayField
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----------
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.. class:: ArrayField(base_field, size=None, **options)
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A field for storing lists of data. Most field types can be used, you simply
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pass another field instance as the :attr:`base_field
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<ArrayField.base_field>`. You may also specify a :attr:`size
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<ArrayField.size>`. ``ArrayField`` can be nested to store multi-dimensional
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arrays.
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.. attribute:: base_field
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This is a required argument.
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Specifies the underlying data type and behavior for the array. It
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should be an instance of a subclass of
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:class:`~django.db.models.Field`. For example, it could be an
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:class:`~django.db.models.IntegerField` or a
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:class:`~django.db.models.CharField`. Most field types are permitted,
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with the exception of those handling relational data
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(:class:`~django.db.models.ForeignKey`,
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:class:`~django.db.models.OneToOneField` and
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:class:`~django.db.models.ManyToManyField`).
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It is possible to nest array fields - you can specify an instance of
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``ArrayField`` as the ``base_field``. For example::
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from django.db import models
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from django.contrib.postgres.fields import ArrayField
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class ChessBoard(models.Model):
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board = ArrayField(
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ArrayField(
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CharField(max_length=10, blank=True, null=True),
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size=8),
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size=8)
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Transformation of values between the database and the model, validation
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of data and configuration, and serialization are all delegated to the
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underlying base field.
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.. attribute:: size
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This is an optional argument.
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If passed, the array will have a maximum size as specified. This will
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be passed to the database, although PostgreSQL at present does not
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enforce the restriction.
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.. note::
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When nesting ``ArrayField``, whether you use the `size` parameter or not,
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PostgreSQL requires that the arrays are rectangular::
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from django.contrib.postgres.fields import ArrayField
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from django.db import models
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class Board(models.Model):
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pieces = ArrayField(ArrayField(models.IntegerField()))
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# Valid
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Board(pieces=[
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[2, 3],
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[2, 1],
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])
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# Not valid
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Board(pieces=[
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[2, 3],
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[2],
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])
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If irregular shapes are required, then the underlying field should be made
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nullable and the values padded with ``None``.
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Querying ArrayField
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^^^^^^^^^^^^^^^^^^^
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There are a number of custom lookups and transforms for :class:`ArrayField`.
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We will use the following example model::
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from django.db import models
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from django.contrib.postgres.fields import ArrayField
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class Post(models.Model):
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name = models.CharField(max_length=200)
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tags = ArrayField(models.CharField(max_length=200), blank=True)
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def __str__(self): # __unicode__ on Python 2
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return self.name
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.. fieldlookup:: arrayfield.contains
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contains
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~~~~~~~~
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The :lookup:`contains` lookup is overridden on :class:`ArrayField`. The
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returned objects will be those where the values passed are a subset of the
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data. It uses the SQL operator ``@>``. For example::
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>>> Post.objects.create(name='First post', tags=['thoughts', 'django'])
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>>> Post.objects.create(name='Second post', tags=['thoughts'])
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>>> Post.objects.create(name='Third post', tags=['tutorial', 'django'])
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>>> Post.objects.filter(tags__contains=['thoughts'])
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[<Post: First post>, <Post: Second post>]
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>>> Post.objects.filter(tags__contains=['django'])
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[<Post: First post>, <Post: Third post>]
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>>> Post.objects.filter(tags__contains=['django', 'thoughts'])
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[<Post: First post>]
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.. fieldlookup:: arrayfield.contained_by
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contained_by
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~~~~~~~~~~~~
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This is the inverse of the :lookup:`contains <arrayfield.contains>` lookup -
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the objects returned will be those where the data is a subset of the values
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passed. It uses the SQL operator ``<@``. For example::
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>>> Post.objects.create(name='First post', tags=['thoughts', 'django'])
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>>> Post.objects.create(name='Second post', tags=['thoughts'])
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>>> Post.objects.create(name='Third post', tags=['tutorial', 'django'])
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>>> Post.objects.filter(tags__contained_by=['thoughts', 'django'])
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[<Post: First post>, <Post: Second post>]
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>>> Post.objects.filter(tags__contained_by=['thoughts', 'django', 'tutorial'])
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[<Post: First post>, <Post: Second post>, <Post: Third post>]
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.. fieldlookup:: arrayfield.overlap
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overlap
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~~~~~~~
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Returns objects where the data shares any results with the values passed. Uses
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the SQL operator ``&&``. For example::
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>>> Post.objects.create(name='First post', tags=['thoughts', 'django'])
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>>> Post.objects.create(name='Second post', tags=['thoughts'])
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>>> Post.objects.create(name='Third post', tags=['tutorial', 'django'])
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>>> Post.objects.filter(tags__overlap=['thoughts'])
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[<Post: First post>, <Post: Second post>]
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>>> Post.objects.filter(tags__overlap=['thoughts', 'tutorial'])
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[<Post: First post>, <Post: Second post>, <Post: Third post>]
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.. fieldlookup:: arrayfield.len
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len
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~~~
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Returns the length of the array. The lookups available afterwards are those
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available for :class:`~django.db.models.IntegerField`. For example::
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>>> Post.objects.create(name='First post', tags=['thoughts', 'django'])
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>>> Post.objects.create(name='Second post', tags=['thoughts'])
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>>> Post.objects.filter(tags__len=1)
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[<Post: Second post>]
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.. fieldlookup:: arrayfield.index
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Index transforms
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~~~~~~~~~~~~~~~~
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This class of transforms allows you to index into the array in queries. Any
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non-negative integer can be used. There are no errors if it exceeds the
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:attr:`size <ArrayField.size>` of the array. The lookups available after the
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transform are those from the :attr:`base_field <ArrayField.base_field>`. For
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example::
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>>> Post.objects.create(name='First post', tags=['thoughts', 'django'])
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>>> Post.objects.create(name='Second post', tags=['thoughts'])
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>>> Post.objects.filter(tags__0='thoughts')
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[<Post: First post>, <Post: Second post>]
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>>> Post.objects.filter(tags__1__iexact='Django')
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[<Post: First post>]
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>>> Post.objects.filter(tags__276='javascript')
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[]
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.. note::
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PostgreSQL uses 1-based indexing for array fields when writing raw SQL.
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However these indexes and those used in :lookup:`slices <arrayfield.slice>`
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use 0-based indexing to be consistent with Python.
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.. fieldlookup:: arrayfield.slice
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Slice transforms
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~~~~~~~~~~~~~~~~
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This class of transforms allow you to take a slice of the array. Any two
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non-negative integers can be used, separated by a single underscore. The
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lookups available after the transform do not change. For example::
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>>> Post.objects.create(name='First post', tags=['thoughts', 'django'])
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>>> Post.objects.create(name='Second post', tags=['thoughts'])
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>>> Post.objects.create(name='Third post', tags=['django', 'python', 'thoughts'])
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>>> Post.objects.filter(tags__0_1=['thoughts'])
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[<Post: First post>]
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>>> Post.objects.filter(tags__0_2__contains='thoughts')
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[<Post: First post>, <Post: Second post>]
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.. note::
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PostgreSQL uses 1-based indexing for array fields when writing raw SQL.
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However these slices and those used in :lookup:`indexes <arrayfield.index>`
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use 0-based indexing to be consistent with Python.
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.. admonition:: Multidimensional arrays with indexes and slices
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PostgreSQL has some rather esoteric behavior when using indexes and slices
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on multidimensional arrays. It will always work to use indexes to reach
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down to the final underlying data, but most other slices behave strangely
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at the database level and cannot be supported in a logical, consistent
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fashion by Django.
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Indexing ArrayField
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^^^^^^^^^^^^^^^^^^^
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At present using :attr:`~django.db.models.Field.db_index` will create a
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``btree`` index. This does not offer particularly significant help to querying.
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A more useful index is a ``GIN`` index, which you should create using a
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:class:`~django.db.migrations.operations.RunSQL` operation.
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HStoreField
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-----------
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.. class:: HStoreField(**options)
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A field for storing mappings of strings to strings. The Python data type
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used is a ``dict``.
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.. note::
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On occasions it may be useful to require or restrict the keys which are
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valid for a given field. This can be done using the
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:class:`~django.contrib.postgres.validators.KeysValidator`.
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Querying HStoreField
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^^^^^^^^^^^^^^^^^^^^
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In addition to the ability to query by key, there are a number of custom
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lookups available for ``HStoreField``.
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We will use the following example model::
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from django.contrib.postgres.fields import HStoreField
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from django.db import models
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class Dog(models.Model):
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name = models.CharField(max_length=200)
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data = HStoreField()
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def __str__(self): # __unicode__ on Python 2
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return self.name
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.. fieldlookup:: hstorefield.key
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Key lookups
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~~~~~~~~~~~
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To query based on a given key, you simply use that key as the lookup name::
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>>> Dog.objects.create(name='Rufus', data={'breed': 'labrador'})
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>>> Dog.objects.create(name='Meg', data={'breed': 'collie'})
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>>> Dog.objects.filter(data__breed='collie')
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[<Dog: Meg>]
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You can chain other lookups after key lookups::
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>>> Dog.objects.filter(data__breed__contains='l')
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[<Dog: Rufus>, <Dog: Meg>]
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If the key you wish to query by clashes with the name of another lookup, you
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need to use the :lookup:`hstorefield.contains` lookup instead.
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.. warning::
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Since any string could be a key in a hstore value, any lookup other than
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those listed below will be interpreted as a key lookup. No errors are
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raised. Be extra careful for typing mistakes, and always check your queries
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work as you intend.
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.. fieldlookup:: hstorefield.contains
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contains
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~~~~~~~~
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The :lookup:`contains` lookup is overridden on
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:class:`~django.contrib.postgres.fields.HStoreField`. The returned objects are
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those where the given ``dict`` of key-value pairs are all contained in the
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field. It uses the SQL operator ``@>``. For example::
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>>> Dog.objects.create(name='Rufus', data={'breed': 'labrador', 'owner': 'Bob'})
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>>> Dog.objects.create(name='Meg', data={'breed': 'collie', 'owner': 'Bob'})
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>>> Dog.objects.create(name='Fred', data={})
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>>> Dog.objects.filter(data__contains={'owner': 'Bob'})
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[<Dog: Rufus>, <Dog: Meg>]
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>>> Dog.objects.filter(data__contains={'breed': 'collie'})
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[<Dog: Meg>]
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.. fieldlookup:: hstorefield.contained_by
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contained_by
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~~~~~~~~~~~~
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This is the inverse of the :lookup:`contains <hstorefield.contains>` lookup -
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the objects returned will be those where the key-value pairs on the object are
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a subset of those in the value passed. It uses the SQL operator ``<@``. For
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example::
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>>> Dog.objects.create(name='Rufus', data={'breed': 'labrador', 'owner': 'Bob'})
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>>> Dog.objects.create(name='Meg', data={'breed': 'collie', 'owner': 'Bob'})
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>>> Dog.objects.create(name='Fred', data={})
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>>> Dog.objects.filter(data__contained_by={'breed': 'collie', 'owner': 'Bob'})
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[<Dog: Meg>, <Dog: Fred>]
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>>> Dog.objects.filter(data__contained_by={'breed': 'collie'})
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[<Dog: Fred>]
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.. fieldlookup:: hstorefield.has_key
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has_key
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~~~~~~~
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Returns objects where the given key is in the data. Uses the SQL operator
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``?``. For example::
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>>> Dog.objects.create(name='Rufus', data={'breed': 'labrador'})
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>>> Dog.objects.create(name='Meg', data={'breed': 'collie', 'owner': 'Bob'})
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>>> Dog.objects.filter(data__has_key='owner')
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[<Dog: Meg>]
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.. fieldlookup:: hstorefield.has_keys
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has_keys
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~~~~~~~~
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Returns objects where all of the given keys are in the data. Uses the SQL operator
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``?&``. For example::
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>>> Dog.objects.create(name='Rufus', data={})
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>>> Dog.objects.create(name='Meg', data={'breed': 'collie', 'owner': 'Bob'})
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>>> Dog.objects.filter(data__has_keys=['breed', 'owner'])
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[<Dog: Meg>]
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.. fieldlookup:: hstorefield.keys
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keys
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~~~~
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Returns objects where the array of keys is the given value. Note that the order
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is not guaranteed to be reliable, so this transform is mainly useful for using
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in conjunction with lookups on
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:class:`~django.contrib.postgres.fields.ArrayField`. Uses the SQL function
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``akeys()``. For example::
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>>> Dog.objects.create(name='Rufus', data={'toy': 'bone'})
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>>> Dog.objects.create(name='Meg', data={'breed': 'collie', 'owner': 'Bob'})
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>>> Dog.objects.filter(data__keys__overlap=['breed', 'toy'])
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[<Dog: Rufus>, <Dog: Meg>]
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.. fieldlookup:: hstorefield.values
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values
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~~~~~~
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Returns objects where the array of values is the given value. Note that the
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order is not guaranteed to be reliable, so this transform is mainly useful for
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using in conjunction with lookups on
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:class:`~django.contrib.postgres.fields.ArrayField`. Uses the SQL function
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``avalues()``. For example::
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>>> Dog.objects.create(name='Rufus', data={'breed': 'labrador'})
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>>> Dog.objects.create(name='Meg', data={'breed': 'collie', 'owner': 'Bob'})
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>>> Dog.objects.filter(data__values__contains=['collie'])
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[<Dog: Meg>]
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