Adapting other PostgreSQL types

PostgreSQL offers other data types which don’t map to native Python types. Psycopg offers wrappers and conversion functions to allow their use.

Composite types casting

Psycopg can adapt PostgreSQL composite types (either created with the CREATE TYPE command or implicitly defined after a table row type) to and from Python tuples or namedtuple.

Before using a composite type it is necessary to get information about it using the CompositeInfo class and to register it using register_composite().

class psycopg.types.composite.CompositeInfo(name, oid, array_oid, field_names, field_types)

Manage information about a composite type.

CompositeInfo is a TypeInfo subclass: check its documentation for generic details.

python_type

After register_composite() is called, it will contain the python type mapping to the registered composite.

psycopg.types.composite.register_composite(info, context=None, factory=None)

Register the adapters to load and dump a composite type.

Parameters
  • info (CompositeInfo) – The object with the information about the composite to register.

  • context (Optional[AdaptContext]) – The context where to register the adapters. If None, register it globally.

  • factory (Optional[Callable[…, Any]]) – Callable to convert the sequence of attributes read from the composite into a Python object.

Note

Registering the adapters doesn’t affect objects already created, even if they are children of the registered context. For instance, registering the adapter globally doesn’t affect already existing connections.

After registering, fetching data of the registered composite will invoke factory to create corresponding Python objects.

If no factory is specified, a namedtuple is created and used to return data.

If the factory is a type (and not a generic callable), then dumpers for that type are created and registered too, so that passing objects of that type to a query will adapt them to the registered type.

Example:

>>> from psycopg.types.composite import CompositeInfo, register_composite

>>> conn.execute("CREATE TYPE card AS (value int, suit text)")

>>> info = CompositeInfo.fetch(conn, "card")
>>> register_composite(info, conn)

>>> my_card = info.python_type(8, "hearts")
>>> my_card
card(value=8, suit='hearts')

>>> conn.execute(
...     "SELECT pg_typeof(%(card)s), (%(card)s).suit", {"card": my_card}
...     ).fetchone()
('card', 'hearts')

>>> conn.execute("SELECT (%s, %s)::card", [1, "spades"]).fetchone()[0]
card(value=1, suit='spades')

Nested composite types are handled as expected, provided that the type of the composite components are registered as well:

>>> conn.execute("CREATE TYPE card_back AS (face card, back text)")

>>> info2 = CompositeInfo.fetch(conn, "card_back")
>>> register_composite(info2, conn)

>>> conn.execute("SELECT ((8, 'hearts'), 'blue')::card_back").fetchone()[0]
card_back(face=card(value=8, suit='hearts'), back='blue')

Range adaptation

PostgreSQL range types are a family of data types representing a range of values between two elements. The type of the element is called the range subtype. PostgreSQL offers a few built-in range types and allows the definition of custom ones.

All the PostgreSQL range types are loaded as the Range Python type, which is a Generic type and can hold bounds of different types.

class psycopg.types.range.Range(lower=None, upper=None, bounds='[)', empty=False)

Python representation for a PostgreSQL range type.

Parameters
  • lower (Optional[~T]) – lower bound for the range. None means unbound

  • upper (Optional[~T]) – upper bound for the range. None means unbound

  • bounds (str) – one of the literal strings (), [), (], [], representing whether the lower or upper bounds are included

  • empty (bool) – if True, the range is empty

This Python type is only used to pass and retrieve range values to and from PostgreSQL and doesn’t attempt to replicate the PostgreSQL range features: it doesn’t perform normalization and doesn’t implement all the operators supported by the database.

PostgreSQL will perform normalisation on Range objects used as query parameters, so, when they are fetched back, they will be found in the normal form (for instance ranges on integers will have [) bounds).

Range objects are immutable, hashable, and support the in operator (checking if an element is within the range). They can be tested for equivalence. Empty ranges evaluate to False in a boolean context, nonempty ones evaluate to True.

Range objects have the following attributes:

isempty: bool

True if the range is empty.

lower: Optional[T]

The lower bound of the range. None if empty or unbound.

upper: Optional[T]

The upper bound of the range. None if empty or unbound.

lower_inc: bool

True if the lower bound is included in the range.

upper_inc: bool

True if the upper bound is included in the range.

lower_inf: bool

True if the range doesn’t have a lower bound.

upper_inf: bool

True if the range doesn’t have an upper bound.

The built-in range objects are adapted automatically: if a Range objects contains date bounds, it is dumped using the daterange OID, and of course daterange values are loaded back as Range[date].

If you create your own range type you can use RangeInfo and register_range() to associate the range type with its subtype and make it work like the builtin ones.

class psycopg.types.range.RangeInfo(name, oid, array_oid, subtype_oid)

Manage information about a range type.

RangeInfo is a TypeInfo subclass: check its documentation for generic details.

psycopg.types.range.register_range(info, context=None)

Register the adapters to load and dump a range type.

Parameters
  • info (RangeInfo) – The object with the information about the range to register.

  • context (Optional[AdaptContext]) – The context where to register the adapters. If None, register it globally.

Register loaders so that loading data of this type will result in a Range with bounds parsed as the right subtype.

Note

Registering the adapters doesn’t affect objects already created, even if they are children of the registered context. For instance, registering the adapter globally doesn’t affect already existing connections.

Example:

>>> from psycopg.types.range import Range, RangeInfo, register_range

>>> conn.execute("CREATE TYPE strrange AS RANGE (SUBTYPE = text)")
>>> info = RangeInfo.fetch(conn, "strrange")
>>> register_range(info, conn)

>>> conn.execute("SELECT pg_typeof(%s)", [Range("a", "z")]).fetchone()[0]
'strrange'

>>> conn.execute("SELECT '[a,z]'::strrange").fetchone()[0]
Range('a', 'z', '[]')

Multirange adaptation

Since PostgreSQL 14, every range type is associated with a multirange, a type representing a disjoint set of ranges. A multirange is automatically available for every range, built-in and user-defined.

All the PostgreSQL range types are loaded as the Multirange Python type, which is a mutable sequence of Range elements.

class psycopg.types.multirange.Multirange(items=())

Python representation for a PostgreSQL multirange type.

Parameters

items (Iterable[Range[~T]]) – Sequence of ranges to initialise the object.

This Python type is only used to pass and retrieve multirange values to and from PostgreSQL and doesn’t attempt to replicate the PostgreSQL multirange features: overlapping items are not merged, empty ranges are not discarded, the items are not ordered, the behaviour of multirange operators is not replicated in Python.

PostgreSQL will perform normalisation on Multirange objects used as query parameters, so, when they are fetched back, they will be found ordered, with overlapping ranges merged, etc.

Multirange objects are a MutableSequence and are totally ordered: they behave pretty much like a list of Range. Like Range, they are Generic on the subtype of their range, so you can declare a variable to be Multirange[date] and mypy will complain if you try to add it a Range[Decimal].

Like for Range, built-in multirange objects are adapted automatically: if a Multirange object contains Range with date bounds, it is dumped using the datemultirange OID, and datemultirange values are loaded back as Multirange[date].

If you have created your own range type you can use MultirangeInfo and register_multirange() to associate the resulting multirange type with its subtype and make it work like the builtin ones.

class psycopg.types.multirange.MultirangeInfo(name, oid, array_oid, range_oid, subtype_oid)

Manage information about a multirange type.

MultirangeInfo is a TypeInfo subclass: check its documentation for generic details.

psycopg.types.multirange.register_multirange(info, context=None)

Register the adapters to load and dump a multirange type.

Parameters
  • info (MultirangeInfo) – The object with the information about the range to register.

  • context (Optional[AdaptContext]) – The context where to register the adapters. If None, register it globally.

Register loaders so that loading data of this type will result in a Range with bounds parsed as the right subtype.

Note

Registering the adapters doesn’t affect objects already created, even if they are children of the registered context. For instance, registering the adapter globally doesn’t affect already existing connections.

Example:

>>> from psycopg.types.multirange import \
...     Multirange, MultirangeInfo, register_multirange
>>> from psycopg.types.range import Range

>>> conn.execute("CREATE TYPE strrange AS RANGE (SUBTYPE = text)")
>>> info = MultirangeInfo.fetch(conn, "strmultirange")
>>> register_multirange(info, conn)

>>> rec = conn.execute(
...     "SELECT pg_typeof(%(mr)s), %(mr)s",
...     {"mr": Multirange([Range("a", "q"), Range("l", "z")])}).fetchone()

>>> rec[0]
'strmultirange'
>>> rec[1]
Multirange([Range('a', 'z', '[)')])

Hstore adaptation

The hstore data type is a key-value store embedded in PostgreSQL. It supports GiST or GIN indexes allowing search by keys or key/value pairs as well as regular BTree indexes for equality, uniqueness etc.

Psycopg can convert Python dict objects to and from hstore structures. Only dictionaries with string keys and values are supported. None is also allowed as value but not as a key.

In order to use the hstore data type it is necessary to load it in a database using:

=# CREATE EXTENSION hstore;

Because hstore is distributed as a contrib module, its oid is not well known, so it is necessary to use TypeInfo.fetch() to query the database and get its oid. The resulting object can be passed to register_hstore() to configure dumping dict to hstore and parsing hstore back to dict, in the context where the adapter is registered.

psycopg.types.hstore.register_hstore(info, context=None)

Register the adapters to load and dump hstore.

Parameters
  • info (TypeInfo) – The object with the information about the hstore type.

  • context (Optional[AdaptContext]) – The context where to register the adapters. If None, register it globally.

Note

Registering the adapters doesn’t affect objects already created, even if they are children of the registered context. For instance, registering the adapter globally doesn’t affect already existing connections.

Example:

>>> from psycopg.types import TypeInfo
>>> from psycopg.types.hstore import register_hstore

>>> info = TypeInfo.fetch(conn, "hstore")
>>> register_hstore(info, conn)

>>> conn.execute("SELECT pg_typeof(%s)", [{"a": "b"}]).fetchone()[0]
'hstore'

>>> conn.execute("SELECT 'foo => bar'::hstore").fetchone()[0]
{'foo': 'bar'}

Geometry adaptation using Shapely

When using the PostGIS extension, it can be useful to retrieve geometry values and have them automatically converted to Shapely instances. Likewise, you may want to store such instances in the database and have the conversion happen automatically.

Warning

Psycopg doesn’t have a dependency on the shapely package: you should install the library as an additional dependency of your project.

Warning

This module is experimental and might be changed in the future according to users’ feedback.

Since PostgGIS is an extension, the geometry type oid is not well known, so it is necessary to use TypeInfo.fetch() to query the database and find it. The resulting object can be passed to register_shapely() to configure dumping shape instances to geometry columns and parsing geometry data back to shape instances, in the context where the adapters are registered.

psycopg.types.shapely.register_shapely(info, context=None)

Register Shapely dumper and loaders.

After invoking this function on an adapter, the queries retrieving PostGIS geometry objects will return Shapely’s shape object instances both in text and binary mode.

Similarly, shape objects can be sent to the database.

This requires the Shapely library to be installed.

Parameters
  • info (TypeInfo) – The object with the information about the geometry type.

  • context (Optional[AdaptContext]) – The context where to register the adapters. If None, register it globally.

Note

Registering the adapters doesn’t affect objects already created, even if they are children of the registered context. For instance, registering the adapter globally doesn’t affect already existing connections.

Example:

>>> from psycopg.types import TypeInfo
>>> from psycopg.types.shapely import register_shapely
>>> from shapely.geometry import Point

>>> info = TypeInfo.fetch(conn, "geometry")
>>> register_shapely(info, conn)

>>> conn.execute("SELECT pg_typeof(%s)", [Point(1.2, 3.4)]).fetchone()[0]
'geometry'

>>> conn.execute("""
... SELECT ST_GeomFromGeoJSON('{
...     "type":"Point",
...     "coordinates":[-48.23456,20.12345]}')
... """).fetchone()[0]
<shapely.geometry.multipolygon.MultiPolygon object at 0x7fb131f3cd90>

Notice that, if the geometry adapters are registered on a specific object (a connection or cursor), other connections and cursors will be unaffected:

>>> conn2 = psycopg.connect(CONN_STR)
>>> conn2.execute("""
... SELECT ST_GeomFromGeoJSON('{
...     "type":"Point",
...     "coordinates":[-48.23456,20.12345]}')
... """).fetchone()[0]
'0101000020E61000009279E40F061E48C0F2B0506B9A1F3440'