`psycopg2.extras` -- Miscellaneous goodies for Psycopg 2 ============================================================= .. sectionauthor:: Daniele Varrazzo .. module:: psycopg2.extras .. testsetup:: import psycopg2.extras from psycopg2.extras import Inet create_test_table() This module is a generic place used to hold little helper functions and classes until a better place in the distribution is found. .. _cursor-subclasses: Connection and cursor subclasses -------------------------------- A few objects that change the way the results are returned by the cursor or modify the object behavior in some other way. Typically `!cursor` subclasses are passed as *cursor_factory* argument to `~psycopg2.connect()` so that the connection's `~connection.cursor()` method will generate objects of this class. Alternatively a `!cursor` subclass can be used one-off by passing it as the *cursor_factory* argument to the `!cursor()` method. If you want to use a `!connection` subclass you can pass it as the *connection_factory* argument of the `!connect()` function. .. index:: pair: Cursor; Dictionary .. _dict-cursor: Dictionary-like cursor ^^^^^^^^^^^^^^^^^^^^^^ The dict cursors allow to access to the retrieved records using an interface similar to the Python dictionaries instead of the tuples. >>> dict_cur = conn.cursor(cursor_factory=psycopg2.extras.DictCursor) >>> dict_cur.execute("INSERT INTO test (num, data) VALUES(%s, %s)", ... (100, "abc'def")) >>> dict_cur.execute("SELECT * FROM test") >>> rec = dict_cur.fetchone() >>> rec['id'] 1 >>> rec['num'] 100 >>> rec['data'] "abc'def" The records still support indexing as the original tuple: >>> rec[2] "abc'def" .. autoclass:: DictCursor .. autoclass:: DictConnection .. note:: Not very useful since Psycopg 2.5: you can use `psycopg2.connect`\ ``(dsn, cursor_factory=DictCursor)`` instead of `!DictConnection`. .. autoclass:: DictRow Real dictionary cursor ^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: RealDictCursor .. autoclass:: RealDictConnection .. note:: Not very useful since Psycopg 2.5: you can use `psycopg2.connect`\ ``(dsn, cursor_factory=RealDictCursor)`` instead of `!RealDictConnection`. .. autoclass:: RealDictRow .. index:: pair: Cursor; namedtuple `namedtuple` cursor ^^^^^^^^^^^^^^^^^^^^ .. versionadded:: 2.3 These objects require :py:func:`collections.namedtuple` to be found, so it is available out-of-the-box only from Python 2.6. Anyway, the namedtuple implementation is compatible with previous Python versions, so all you have to do is to `download it`__ and make it available where we expect it to be... :: from somewhere import namedtuple import collections collections.namedtuple = namedtuple from psycopg.extras import NamedTupleConnection # ... .. __: http://code.activestate.com/recipes/500261-named-tuples/ .. autoclass:: NamedTupleCursor .. autoclass:: NamedTupleConnection .. note:: Not very useful since Psycopg 2.5: you can use `psycopg2.connect`\ ``(dsn, cursor_factory=NamedTupleCursor)`` instead of `!NamedTupleConnection`. .. index:: pair: Cursor; Logging Logging cursor ^^^^^^^^^^^^^^ .. autoclass:: LoggingConnection :members: initialize,filter .. autoclass:: LoggingCursor .. autoclass:: MinTimeLoggingConnection :members: initialize,filter .. autoclass:: MinTimeLoggingCursor .. index:: single: Data types; Additional Additional data types --------------------- .. index:: pair: JSON; Data types pair: JSON; Adaptation .. _adapt-json: JSON_ adaptation ^^^^^^^^^^^^^^^^ .. versionadded:: 2.5 Psycopg can adapt Python objects to and from the PostgreSQL |pgjson|_ type. With PostgreSQL 9.2 adaptation is available out-of-the-box. To use JSON data with previous database versions (either with the `9.1 json extension`__, but even if you want to convert text fields to JSON) you can use `register_json()`. .. __: http://people.planetpostgresql.org/andrew/index.php?/archives/255-JSON-for-PG-9.2-...-and-now-for-9.1!.html The Python library used to convert Python objects to JSON depends on the language version: with Python 2.6 and following the :py:mod:`json` module from the standard library is used; with previous versions the `simplejson`_ module is used if available. Note that the last `!simplejson` version supporting Python 2.4 is the 2.0.9. .. _JSON: http://www.json.org/ .. |pgjson| replace:: :sql:`json` .. _pgjson: http://www.postgresql.org/docs/current/static/datatype-json.html .. _simplejson: http://pypi.python.org/pypi/simplejson/ In order to pass a Python object to the database as query argument you can use the `Json` adapter:: curs.execute("insert into mytable (jsondata) values (%s)", [Json({'a': 100})]) Reading from the database, |pgjson| values will be automatically converted to Python objects. .. note:: If you are using the PostgreSQL :sql:`json` data type but you want to read it as string in Python instead of having it parsed, your can either cast the column to :sql:`text` in the query (it is an efficient operation, that doesn't involve a copy):: cur.execute("select jsondata::text from mytable") or you can register a no-op `!loads()` function with `register_default_json()`:: psycopg2.extras.register_default_json(loads=lambda x: x) .. note:: You can use `~psycopg2.extensions.register_adapter()` to adapt any Python dictionary to JSON, either registering `Json` or any subclass or factory creating a compatible adapter:: psycopg2.extensions.register_adapter(dict, psycopg2.extras.Json) This setting is global though, so it is not compatible with similar adapters such as the one registered by `register_hstore()`. Any other object supported by JSON can be registered the same way, but this will clobber the default adaptation rule, so be careful to unwanted side effects. If you want to customize the adaptation from Python to PostgreSQL you can either provide a custom `!dumps()` function to `Json`:: curs.execute("insert into mytable (jsondata) values (%s)", [Json({'a': 100}, dumps=simplejson.dumps)]) or you can subclass it overriding the `~Json.dumps()` method:: class MyJson(Json): def dumps(self, obj): return simplejson.dumps(obj) curs.execute("insert into mytable (jsondata) values (%s)", [MyJson({'a': 100})]) Customizing the conversion from PostgreSQL to Python can be done passing a custom `!loads()` function to `register_json()` (or `register_default_json()` for PostgreSQL 9.2). For example, if you want to convert the float values from :sql:`json` into :py:class:`~decimal.Decimal` you can use:: loads = lambda x: json.loads(x, parse_float=Decimal) psycopg2.extras.register_json(conn, loads=loads) .. autoclass:: Json .. automethod:: dumps .. autofunction:: register_json .. versionchanged:: 2.5.4 added the *name* parameter to enable :sql:`jsonb` support. .. autofunction:: register_default_json .. index:: pair: hstore; Data types pair: dict; Adaptation .. _adapt-hstore: Hstore data type ^^^^^^^^^^^^^^^^ .. versionadded:: 2.3 The |hstore|_ data type is a key-value store embedded in PostgreSQL. It has been available for several server versions but with the release 9.0 it has been greatly improved in capacity and usefulness with the addition of many functions. 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/unicode keys and values are supported. `!None` is also allowed as value but not as a key. Psycopg uses a more efficient |hstore| representation when dealing with PostgreSQL 9.0 but previous server versions are supported as well. By default the adapter/typecaster are disabled: they can be enabled using the `register_hstore()` function. .. autofunction:: register_hstore .. versionchanged:: 2.4 added the *oid* parameter. If not specified, the typecaster is installed also if |hstore| is not installed in the :sql:`public` schema. .. versionchanged:: 2.4.3 added support for |hstore| array. .. |hstore| replace:: :sql:`hstore` .. _hstore: http://www.postgresql.org/docs/current/static/hstore.html .. index:: pair: Composite types; Data types pair: tuple; Adaptation pair: namedtuple; Adaptation .. _adapt-composite: Composite types casting ^^^^^^^^^^^^^^^^^^^^^^^ .. versionadded:: 2.4 Using `register_composite()` it is possible to cast a PostgreSQL composite type (either created with the |CREATE TYPE|_ command or implicitly defined after a table row type) into a Python named tuple, or into a regular tuple if :py:func:`collections.namedtuple` is not found. .. |CREATE TYPE| replace:: :sql:`CREATE TYPE` .. _CREATE TYPE: http://www.postgresql.org/docs/current/static/sql-createtype.html .. doctest:: >>> cur.execute("CREATE TYPE card AS (value int, suit text);") >>> psycopg2.extras.register_composite('card', cur) >>> cur.execute("select (8, 'hearts')::card") >>> cur.fetchone()[0] card(value=8, suit='hearts') Nested composite types are handled as expected, provided that the type of the composite components are registered as well. .. doctest:: >>> cur.execute("CREATE TYPE card_back AS (face card, back text);") >>> psycopg2.extras.register_composite('card_back', cur) >>> cur.execute("select ((8, 'hearts'), 'blue')::card_back") >>> cur.fetchone()[0] card_back(face=card(value=8, suit='hearts'), back='blue') Adaptation from Python tuples to composite types is automatic instead and requires no adapter registration. .. _custom-composite: .. Note:: If you want to convert PostgreSQL composite types into something different than a `!namedtuple` you can subclass the `CompositeCaster` overriding `~CompositeCaster.make()`. For example, if you want to convert your type into a Python dictionary you can use:: >>> class DictComposite(psycopg2.extras.CompositeCaster): ... def make(self, values): ... return dict(zip(self.attnames, values)) >>> psycopg2.extras.register_composite('card', cur, ... factory=DictComposite) >>> cur.execute("select (8, 'hearts')::card") >>> cur.fetchone()[0] {'suit': 'hearts', 'value': 8} .. autofunction:: register_composite .. versionchanged:: 2.4.3 added support for array of composite types .. versionchanged:: 2.5 added the *factory* parameter .. autoclass:: CompositeCaster .. automethod:: make .. versionadded:: 2.5 Object attributes: .. attribute:: name The name of the PostgreSQL type. .. attribute:: schema The schema where the type is defined. .. versionadded:: 2.5 .. attribute:: oid The oid of the PostgreSQL type. .. attribute:: array_oid The oid of the PostgreSQL array type, if available. .. attribute:: type The type of the Python objects returned. If :py:func:`collections.namedtuple()` is available, it is a named tuple with attributes equal to the type components. Otherwise it is just the `!tuple` object. .. attribute:: attnames List of component names of the type to be casted. .. attribute:: atttypes List of component type oids of the type to be casted. .. index:: pair: range; Data types .. _adapt-range: Range data types ^^^^^^^^^^^^^^^^ .. versionadded:: 2.5 Psycopg offers a `Range` Python type and supports adaptation between them and PostgreSQL |range|_ types. Builtin |range| types are supported out-of-the-box; user-defined |range| types can be adapted using `register_range()`. .. |range| replace:: :sql:`range` .. _range: http://www.postgresql.org/docs/current/static/rangetypes.html .. autoclass:: Range 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. .. __: http://www.postgresql.org/docs/current/static/functions-range.html#RANGE-OPERATORS-TABLE `!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 boolean context, nonempty evaluate to `!True`. .. versionchanged:: 2.5.3 `!Range` objects can be sorted although, as on the server-side, this ordering is not particularly meangingful. It is only meant to be used by programs assuming objects using `!Range` as primary key can be sorted on them. In previous versions comparing `!Range`\s raises `!TypeError`. Although it is possible to instantiate `!Range` objects, the class doesn't have an adapter registered, so you cannot normally pass these instances as query arguments. To use range objects as query arguments you can either use one of the provided subclasses, such as `NumericRange` or create a custom subclass using `register_range()`. Object attributes: .. autoattribute:: isempty .. autoattribute:: lower .. autoattribute:: upper .. autoattribute:: lower_inc .. autoattribute:: upper_inc .. autoattribute:: lower_inf .. autoattribute:: upper_inf The following `Range` subclasses map builtin PostgreSQL |range| types to Python objects: they have an adapter registered so their instances can be passed as query arguments. |range| values read from database queries are automatically casted into instances of these classes. .. autoclass:: NumericRange .. autoclass:: DateRange .. autoclass:: DateTimeRange .. autoclass:: DateTimeTZRange .. note:: Python lacks a representation for :sql:`infinity` date so Psycopg converts the value to `date.max` and such. When written into the database these dates will assume their literal value (e.g. :sql:`9999-12-31` instead of :sql:`infinity`). Check :ref:`infinite-dates-handling` for an example of an alternative adapter to map `date.max` to :sql:`infinity`. An alternative dates adapter will be used automatically by the `DateRange` adapter and so on. Custom |range| types (created with |CREATE TYPE|_ :sql:`... AS RANGE`) can be adapted to a custom `Range` subclass: .. autofunction:: register_range .. autoclass:: RangeCaster Object attributes: .. attribute:: range The `!Range` subclass adapted. .. attribute:: adapter The `~psycopg2.extensions.ISQLQuote` responsible to adapt `!range`. .. attribute:: typecaster The object responsible for casting. .. attribute:: array_typecaster The object responsible to cast arrays, if available, else `!None`. .. index:: pair: UUID; Data types .. _adapt-uuid: UUID data type ^^^^^^^^^^^^^^ .. versionadded:: 2.0.9 .. versionchanged:: 2.0.13 added UUID array support. .. doctest:: >>> psycopg2.extras.register_uuid() >>> # Python UUID can be used in SQL queries >>> import uuid >>> my_uuid = uuid.UUID('{12345678-1234-5678-1234-567812345678}') >>> psycopg2.extensions.adapt(my_uuid).getquoted() "'12345678-1234-5678-1234-567812345678'::uuid" >>> # PostgreSQL UUID are transformed into Python UUID objects. >>> cur.execute("SELECT 'a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11'::uuid") >>> cur.fetchone()[0] UUID('a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11') .. autofunction:: register_uuid .. autoclass:: UUID_adapter .. index:: pair: INET; Data types :sql:`inet` data type ^^^^^^^^^^^^^^^^^^^^^^ .. versionadded:: 2.0.9 .. versionchanged:: 2.4.5 added inet array support. .. doctest:: >>> psycopg2.extras.register_inet() >>> cur.mogrify("SELECT %s", (Inet('127.0.0.1/32'),)) "SELECT E'127.0.0.1/32'::inet" >>> cur.execute("SELECT '192.168.0.1/24'::inet") >>> cur.fetchone()[0].addr '192.168.0.1/24' .. autofunction:: register_inet .. autoclass:: Inet .. index:: single: Time zones; Fractional Fractional time zones --------------------- .. autofunction:: register_tstz_w_secs .. versionadded:: 2.0.9 .. versionchanged:: 2.2.2 function is no-op: see :ref:`tz-handling`. .. index:: pair: Example; Coroutine; Coroutine support ----------------- .. autofunction:: wait_select(conn)