psycopg2.extras – Miscellaneous goodies for Psycopg 2

This module is a generic place used to hold little helper functions and classes until a better place in the distribution is found.

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 connect() so that the connection’s 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.

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"
class psycopg2.extras.DictCursor(*args, **kwargs)

A cursor that keeps a list of column name -> index mappings.

class psycopg2.extras.DictConnection

A connection that uses DictCursor automatically.

Note

Not very useful since Psycopg 2.5: you can use psycopg2.connect(dsn, cursor_factory=DictCursor) instead of DictConnection.

class psycopg2.extras.DictRow(cursor)

A row object that allow by-column-name access to data.

Real dictionary cursor

class psycopg2.extras.RealDictCursor(*args, **kwargs)

A cursor that uses a real dict as the base type for rows.

Note that this cursor is extremely specialized and does not allow the normal access (using integer indices) to fetched data. If you need to access database rows both as a dictionary and a list, then use the generic DictCursor instead of RealDictCursor.

class psycopg2.extras.RealDictConnection

A connection that uses RealDictCursor automatically.

Note

Not very useful since Psycopg 2.5: you can use psycopg2.connect(dsn, cursor_factory=RealDictCursor) instead of RealDictConnection.

class psycopg2.extras.RealDictRow(cursor)

A dict subclass representing a data record.

namedtuple cursor

New in version 2.3.

These objects require 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
# ...
class psycopg2.extras.NamedTupleCursor

A cursor that generates results as namedtuple.

fetch*() methods will return named tuples instead of regular tuples, so their elements can be accessed both as regular numeric items as well as attributes.

>>> nt_cur = conn.cursor(cursor_factory=psycopg2.extras.NamedTupleCursor)
>>> rec = nt_cur.fetchone()
>>> rec
Record(id=1, num=100, data="abc'def")
>>> rec[1]
100
>>> rec.data
"abc'def"
class psycopg2.extras.NamedTupleConnection

A connection that uses NamedTupleCursor automatically.

Note

Not very useful since Psycopg 2.5: you can use psycopg2.connect(dsn, cursor_factory=NamedTupleCursor) instead of NamedTupleConnection.

Logging cursor

class psycopg2.extras.LoggingConnection

A connection that logs all queries to a file or logger object.

filter(msg, curs)

Filter the query before logging it.

This is the method to overwrite to filter unwanted queries out of the log or to add some extra data to the output. The default implementation just does nothing.

initialize(logobj)

Initialize the connection to log to logobj.

The logobj parameter can be an open file object or a Logger instance from the standard logging module.

class psycopg2.extras.LoggingCursor

A cursor that logs queries using its connection logging facilities.

class psycopg2.extras.MinTimeLoggingConnection

A connection that logs queries based on execution time.

This is just an example of how to sub-class LoggingConnection to provide some extra filtering for the logged queries. Both the initialize() and filter() methods are overwritten to make sure that only queries executing for more than mintime ms are logged.

Note that this connection uses the specialized cursor MinTimeLoggingCursor.

class psycopg2.extras.MinTimeLoggingCursor

The cursor sub-class companion to MinTimeLoggingConnection.

Additional data types

JSON adaptation

New in version 2.5.

Changed in version 2.5.4: added jsonb support. In previous versions jsonb values are returned as strings. See the FAQ for a workaround.

Psycopg can adapt Python objects to and from the PostgreSQL json and jsonb types. With PostgreSQL 9.2 and following versions 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 the register_json() function.

The Python library used by default to convert Python objects to JSON and to parse data from the database depends on the language version: with Python 2.6 and following the 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.

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, json and jsonb values will be automatically converted to Python objects.

Note

If you are using the PostgreSQL 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 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 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 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(). For the builtin data types (json from PostgreSQL 9.2, jsonb from PostgreSQL 9.4) use register_default_json() and register_default_jsonb(). For example, if you want to convert the float values from json into Decimal you can use:

loads = lambda x: json.loads(x, parse_float=Decimal)
psycopg2.extras.register_json(conn, loads=loads)
class psycopg2.extras.Json(adapted, dumps=None)

An ISQLQuote wrapper to adapt a Python object to json data type.

Json can be used to wrap any object supported by the provided dumps function. If none is provided, the standard json.dumps() is used (simplejson for Python < 2.6; getquoted() will raise ImportError if the module is not available).

dumps(obj)

Serialize obj in JSON format.

The default is to call json.dumps() or the dumps function provided in the constructor. You can override this method to create a customized JSON wrapper.

psycopg2.extras.register_json(conn_or_curs=None, globally=False, loads=None, oid=None, array_oid=None, name='json')

Create and register typecasters converting json type to Python objects.

Parameters:
  • conn_or_curs – a connection or cursor used to find the json and json[] oids; the typecasters are registered in a scope limited to this object, unless globally is set to True. It can be None if the oids are provided
  • globally – if False register the typecasters only on conn_or_curs, otherwise register them globally
  • loads – the function used to parse the data into a Python object. If None use json.loads(), where json is the module chosen according to the Python version (see above)
  • oid – the OID of the json type if known; If not, it will be queried on conn_or_curs
  • array_oid – the OID of the json[] array type if known; if not, it will be queried on conn_or_curs
  • name – the name of the data type to look for in conn_or_curs

The connection or cursor passed to the function will be used to query the database and look for the OID of the json type (or an alternative type if name if provided). No query is performed if oid and array_oid are provided. Raise ProgrammingError if the type is not found.

Changed in version 2.5.4: added the name parameter to enable jsonb support.

psycopg2.extras.register_default_json(conn_or_curs=None, globally=False, loads=None)

Create and register json typecasters for PostgreSQL 9.2 and following.

Since PostgreSQL 9.2 json is a builtin type, hence its oid is known and fixed. This function allows specifying a customized loads function for the default json type without querying the database. All the parameters have the same meaning of register_json().

psycopg2.extras.register_default_jsonb(conn_or_curs=None, globally=False, loads=None)

Create and register jsonb typecasters for PostgreSQL 9.4 and following.

As in register_default_json(), the function allows to register a customized loads function for the jsonb type at its known oid for PostgreSQL 9.4 and following versions. All the parameters have the same meaning of register_json().

New in version 2.5.4.

Hstore data type

New in version 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.

psycopg2.extras.register_hstore(conn_or_curs, globally=False, unicode=False, oid=None, array_oid=None)

Register adapter and typecaster for dict-hstore conversions.

Parameters:
  • conn_or_curs – a connection or cursor: the typecaster will be registered only on this object unless globally is set to True
  • globally – register the adapter globally, not only on conn_or_curs
  • unicode – if True, keys and values returned from the database will be unicode instead of str. The option is not available on Python 3
  • oid – the OID of the hstore type if known. If not, it will be queried on conn_or_curs.
  • array_oid – the OID of the hstore array type if known. If not, it will be queried on conn_or_curs.

The connection or cursor passed to the function will be used to query the database and look for the OID of the hstore type (which may be different across databases). If querying is not desirable (e.g. with asynchronous connections) you may specify it in the oid parameter, which can be found using a query such as SELECT 'hstore'::regtype::oid. Analogously you can obtain a value for array_oid using a query such as SELECT 'hstore[]'::regtype::oid.

Note that, when passing a dictionary from Python to the database, both strings and unicode keys and values are supported. Dictionaries returned from the database have keys/values according to the unicode parameter.

The hstore contrib module must be already installed in the database (executing the hstore.sql script in your contrib directory). Raise ProgrammingError if the type is not found.

Changed in version 2.4: added the oid parameter. If not specified, the typecaster is installed also if hstore is not installed in the public schema.

Changed in version 2.4.3: added support for hstore array.

Composite types casting

New in version 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 collections.namedtuple() is not found.

>>> cur.execute("CREATE TYPE card AS (value int, suit text);")
>>> psycopg2.extras.register_composite('card', cur)
<psycopg2.extras.CompositeCaster object at 0x...>

>>> 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.

>>> cur.execute("CREATE TYPE card_back AS (face card, back text);")
>>> psycopg2.extras.register_composite('card_back', cur)
<psycopg2.extras.CompositeCaster object at 0x...>

>>> 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.

Note

If you want to convert PostgreSQL composite types into something different than a namedtuple you can subclass the CompositeCaster overriding 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}
psycopg2.extras.register_composite(name, conn_or_curs, globally=False, factory=None)

Register a typecaster to convert a composite type into a tuple.

Parameters:
  • name – the name of a PostgreSQL composite type, e.g. created using the CREATE TYPE command
  • conn_or_curs – a connection or cursor used to find the type oid and components; the typecaster is registered in a scope limited to this object, unless globally is set to True
  • globally – if False (default) register the typecaster only on conn_or_curs, otherwise register it globally
  • factory – if specified it should be a CompositeCaster subclass: use it to customize how to cast composite types
Returns:

the registered CompositeCaster or factory instance responsible for the conversion

Changed in version 2.4.3: added support for array of composite types

Changed in version 2.5: added the factory parameter

class psycopg2.extras.CompositeCaster(name, oid, attrs, array_oid=None, schema=None)

Helps conversion of a PostgreSQL composite type into a Python object.

The class is usually created by the register_composite() function. You may want to create and register manually instances of the class if querying the database at registration time is not desirable (such as when using an asynchronous connections).

make(values)

Return a new Python object representing the data being casted.

values is the list of attributes, already casted into their Python representation.

You can subclass this method to customize the composite cast.

New in version 2.5.

Object attributes:

name

The name of the PostgreSQL type.

schema

The schema where the type is defined.

New in version 2.5.

oid

The oid of the PostgreSQL type.

array_oid

The oid of the PostgreSQL array type, if available.

type

The type of the Python objects returned. If collections.namedtuple() is available, it is a named tuple with attributes equal to the type components. Otherwise it is just the tuple object.

attnames

List of component names of the type to be casted.

atttypes

List of component type oids of the type to be casted.

Range data types

New in version 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().

class psycopg2.extras.Range(lower=None, upper=None, bounds='[)', empty=False)

Python representation for a PostgreSQL range type.

Parameters:
  • lower – lower bound for the range. None means unbound
  • upper – upper bound for the range. None means unbound
  • bounds – one of the literal strings (), [), (], [], representing whether the lower or upper bounds are included
  • empty – 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.

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.

Changed in version 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 Ranges 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:

isempty

True if the range is empty.

lower

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

upper

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

lower_inc

True if the lower bound is included in the range.

upper_inc

True if the upper bound is included in the range.

lower_inf

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

upper_inf

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

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.

class psycopg2.extras.NumericRange(lower=None, upper=None, bounds='[)', empty=False)

A Range suitable to pass Python numeric types to a PostgreSQL range.

PostgreSQL types int4range, int8range, numrange are casted into NumericRange instances.

class psycopg2.extras.DateRange(lower=None, upper=None, bounds='[)', empty=False)

Represents daterange values.

class psycopg2.extras.DateTimeRange(lower=None, upper=None, bounds='[)', empty=False)

Represents tsrange values.

class psycopg2.extras.DateTimeTZRange(lower=None, upper=None, bounds='[)', empty=False)

Represents tstzrange values.

Note

Python lacks a representation for 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. 9999-12-31 instead of infinity). Check Infinite dates handling for an example of an alternative adapter to map date.max to infinity. An alternative dates adapter will be used automatically by the DateRange adapter and so on.

Custom range types (created with CREATE TYPE ... AS RANGE) can be adapted to a custom Range subclass:

psycopg2.extras.register_range(pgrange, pyrange, conn_or_curs, globally=False)

Create and register an adapter and the typecasters to convert between a PostgreSQL range type and a PostgreSQL Range subclass.

Parameters:
  • pgrange – the name of the PostgreSQL range type. Can be schema-qualified
  • pyrange – a Range strict subclass, or just a name to give to a new class
  • conn_or_curs – a connection or cursor used to find the oid of the range and its subtype; the typecaster is registered in a scope limited to this object, unless globally is set to True
  • globally – if False (default) register the typecaster only on conn_or_curs, otherwise register it globally
Returns:

RangeCaster instance responsible for the conversion

If a string is passed to pyrange, a new Range subclass is created with such name and will be available as the range attribute of the returned RangeCaster object.

The function queries the database on conn_or_curs to inspect the pgrange type and raises ProgrammingError if the type is not found. If querying the database is not advisable, use directly the RangeCaster class and register the adapter and typecasters using the provided functions.

class psycopg2.extras.RangeCaster(pgrange, pyrange, oid, subtype_oid, array_oid=None)

Helper class to convert between Range and PostgreSQL range types.

Objects of this class are usually created by register_range(). Manual creation could be useful if querying the database is not advisable: in this case the oids must be provided.

Object attributes:

range

The Range subclass adapted.

adapter

The ISQLQuote responsible to adapt range.

typecaster

The object responsible for casting.

array_typecaster

The object responsible to cast arrays, if available, else None.

UUID data type

New in version 2.0.9.

Changed in version 2.0.13: added UUID array support.

>>> psycopg2.extras.register_uuid()
<psycopg2._psycopg.type object at 0x...>

>>> # 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')
psycopg2.extras.register_uuid(oids=None, conn_or_curs=None)

Create the UUID type and an uuid.UUID adapter.

Parameters:
  • oids – oid for the PostgreSQL uuid type, or 2-items sequence with oids of the type and the array. If not specified, use PostgreSQL standard oids.
  • conn_or_curs – where to register the typecaster. If not specified, register it globally.
class psycopg2.extras.UUID_adapter(uuid)

Adapt Python’s uuid.UUID type to PostgreSQL’s uuid.

inet data type

New in version 2.0.9.

Changed in version 2.4.5: added inet array support.

>>> psycopg2.extras.register_inet()
<psycopg2._psycopg.type object at 0x...>

>>> 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'
psycopg2.extras.register_inet(oid=None, conn_or_curs=None)

Create the INET type and an Inet adapter.

Parameters:
  • oid – oid for the PostgreSQL inet type, or 2-items sequence with oids of the type and the array. If not specified, use PostgreSQL standard oids.
  • conn_or_curs – where to register the typecaster. If not specified, register it globally.
class psycopg2.extras.Inet(addr)

Wrap a string to allow for correct SQL-quoting of inet values.

Note that this adapter does NOT check the passed value to make sure it really is an inet-compatible address but DOES call adapt() on it to make sure it is impossible to execute an SQL-injection by passing an evil value to the initializer.

Fractional time zones

psycopg2.extras.register_tstz_w_secs(oids=None, conn_or_curs=None)

The function used to register an alternate type caster for TIMESTAMP WITH TIME ZONE to deal with historical time zones with seconds in the UTC offset.

These are now correctly handled by the default type caster, so currently the function doesn’t do anything.

New in version 2.0.9.

Changed in version 2.2.2: function is no-op: see Time zones handling.

Coroutine support

psycopg2.extras.wait_select(conn)

Wait until a connection or cursor has data available.

The function is an example of a wait callback to be registered with set_wait_callback(). This function uses select() to wait for data available.