This module is a generic place used to hold little helper functions and classes until a better place in the distribution is found.
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.
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"
A cursor that keeps a list of column name -> index mappings.
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.
A row object that allow by-column-name access to data.
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.
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.
A dict subclass representing a data record.
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
# ...
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"
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.
A connection that logs all queries to a file or logger object.
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 the connection to log to logobj.
The logobj parameter can be an open file object or a Logger instance from the standard logging module.
A cursor that logs queries using its connection logging facilities.
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.
The cursor sub-class companion to MinTimeLoggingConnection.
New in version 2.5.
Psycopg can adapt Python objects to and from the PostgreSQL json 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().
The Python library used to convert Python objects to JSON 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 values will be automatically converted to Python objects.
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() (or register_default_json() for PostgreSQL 9.2). 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)
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).
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.
Create and register typecasters converting json type to Python objects.
| Parameters: |
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|---|
The connection or cursor passed to the function will be used to query the database and look for the OID of the json type. No query is performed if oid and array_oid are provided. Raise ProgrammingError if the type is not found.
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().
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.
Register adapter and typecaster for dict-hstore conversions.
| Parameters: |
|
|---|
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.
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}
Register a typecaster to convert a composite type into a tuple.
| Parameters: |
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|---|---|
| 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
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).
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:
The name of the PostgreSQL type.
The schema where the type is defined.
New in version 2.5.
The oid of the PostgreSQL type.
The oid of the PostgreSQL array type, if available.
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.
List of component names of the type to be casted.
List of component type oids of the type to be casted.
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().
Python representation for a PostgreSQL range type.
| Parameters: |
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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 but not for ordering. Empty ranges evaluate to False in boolean context, nonempty evaluate to True.
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:
True if the range is empty.
The lower bound of the range. None if empty or unbound.
The upper bound of the range. None if empty or unbound.
True if the lower bound is included in the range.
True if the upper bound is included in the range.
True if the range doesn’t have a lower bound.
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.
A Range suitable to pass Python numeric types to a PostgreSQL range.
PostgreSQL types int4range, int8range, numrange are casted into NumericRange instances.
Represents daterange values.
Represents tsrange values.
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:
Create and register an adapter and the typecasters to convert between a PostgreSQL range type and a PostgreSQL Range subclass.
| Parameters: |
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|---|---|
| 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.
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:
The Range subclass adapted.
The object responsible for casting.
The object responsible to cast arrays, if available, else None.
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')
Create the UUID type and an uuid.UUID adapter.
| Parameters: |
|
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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'
Create the INET type and an Inet adapter.
| Parameters: |
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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.
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.
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.