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300 lines
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ReStructuredText
300 lines
10 KiB
ReStructuredText
More advanced topics
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====================
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.. sectionauthor:: Daniele Varrazzo <daniele.varrazzo@gmail.com>
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.. index::
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double: Subclassing; Cursor
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double: Subclassing; Connection
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.. _subclassing-connection:
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.. _subclassing-cursor:
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Connection and cursor factories
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-------------------------------
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Psycopg exposes two new-style classes that can be sub-classed and expanded to
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adapt them to the needs of the programmer: :class:`psycopg2.extensions.cursor`
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and :class:`psycopg2.extensions.connection`. The :class:`connection` class is
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usually sub-classed only to provide an easy way to create customized cursors
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but other uses are possible. :class:`cursor` is much more interesting, because
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it is the class where query building, execution and result type-casting into
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Python variables happens.
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An example of cursor subclass performing logging is::
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import psycopg2
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import psycopg2.extensions
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import logging
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class LoggingCursor(psycopg2.extensions.cursor):
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def execute(self, sql, args=None):
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logger = logging.getLogger('sql_debug')
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logger.info(self.mogrify(sql, args))
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try:
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psycopg2.extensions.cursor.execute(self, sql, args)
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except Exception, exc:
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logger.error("%s: %s" % (exc.__class__.__name__, exc))
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raise
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conn = psycopg2.connect(DSN)
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curs = conn.cursor(cursor_factory=LoggingCursor)
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curs.execute("INSERT INTO mytable VALUES (%s, %s, %s);",
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(10, 20, 30))
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.. index::
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single: Objects; Creating new adapters
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single: Adaptation; Creating new adapters
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single: Data types; Creating new adapters
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.. _adapting-new-types:
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Adapting new Python types to SQL syntax
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---------------------------------------
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Any Python class or type can be adapted to an SQL string. Adaptation mechanism
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is similar to the Object Adaptation proposed in the :pep:`246` and is exposed
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by the :func:`psycopg2.extensions.adapt()` function.
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The :meth:`cursor.execute()` method adapts its arguments to the
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:class:`psycopg2.extensions.ISQLQuote` protocol. Objects that conform to this
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protocol expose a ``getquoted()`` method returning the SQL representation of
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the object as a string.
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The easiest way to adapt an object to an SQL string is to register an adapter
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function via the :func:`psycopg2.extensions.register_adapter()` function. The
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adapter function must take the value to be adapted as argument and return a
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conform object. A convenient object is the :func:`psycopg2.extensions.AsIs`
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wrapper, whose ``getquoted()`` result is simply the ``str()``\ ingification of
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the wrapped object.
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Example: mapping of a ``Point`` class into the ``point`` PostgreSQL geometric
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type::
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from psycopg2.extensions import adapt, register_adapter, AsIs
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class Point(object):
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def __init__(self, x, y):
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self.x = x
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self.y = y
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def adapt_point(point):
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return AsIs("'(%s, %s)'" % (adapt(point.x), adapt(point.y)))
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register_adapter(Point, adapt_point)
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curs.execute("INSERT INTO atable (apoint) VALUES (%s)",
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(Point(1.23, 4.56),))
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The above function call results in the SQL command::
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INSERT INTO atable (apoint) VALUES ((1.23, 4.56));
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.. index:: Type casting
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.. _type-casting-from-sql-to-python:
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Type casting of SQL types into Python values
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--------------------------------------------
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PostgreSQL objects read from the database can be adapted to Python objects
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through an user-defined adapting function. An adapter function takes two
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arguments: the object string representation as returned by PostgreSQL and the
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cursor currently being read, and should return a new Python object. For
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example, the following function parses a PostgreSQL ``point`` into the
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previously defined ``Point`` class::
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def cast_point(value, curs):
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if value is not None:
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# Convert from (f1, f2) syntax using a regular expression.
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m = re.match("\((.*),(.*)\)", value)
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if m:
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return Point(float(m.group(1)), float(m.group(2)))
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To create a mapping from the PostgreSQL type (either standard or user-defined),
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its ``oid`` must be known. It can be retrieved either by the second column of
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the cursor description::
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curs.execute("SELECT NULL::point")
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point_oid = curs.description[0][1] # usually returns 600
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or by querying the system catalogs for the type name and namespace (the
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namespace for system objects is ``pg_catalog``)::
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curs.execute("""
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SELECT pg_type.oid
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FROM pg_type JOIN pg_namespace
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ON typnamespace = pg_namespace.oid
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WHERE typname = %(typename)s
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AND nspname = %(namespace)s""",
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{'typename': 'point', 'namespace': 'pg_catalog'})
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point_oid = curs.fetchone()[0]
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After you know the object ``oid``, you must can and register the new type::
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POINT = psycopg2.extensions.new_type((point_oid,), "POINT", cast_point)
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psycopg2.extensions.register_type(POINT)
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The :func:`psycopg2.extensions.new_type()` function binds the object oids
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(more than one can be specified) to the adapter function.
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:func:`psycopg2.extensions.register_type()` completes the spell. Conversion
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is automatically performed when a column whose type is a registered ``oid`` is
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read::
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>>> curs.execute("SELECT '(10.2,20.3)'::point")
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>>> point = curs.fetchone()[0]
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>>> print type(point), point.x, point.y
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<class '__main__.Point'> 10.2 20.3
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.. index::
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pair: Asynchronous; Notifications
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pair: LISTEN; SQL command
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pair: NOTIFY; SQL command
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.. _async-notify:
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Asynchronous notifications
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--------------------------
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Psycopg allows asynchronous interaction with other database sessions using the
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facilities offered by PostgreSQL commands |LISTEN|_ and |NOTIFY|_. Please
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refer to the PostgreSQL documentation for examples of how to use this form of
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communications.
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Notifications received are made available in the :attr:`connection.notifies`
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list. Notifications can be sent from Python code simply using a ``NOTIFY``
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command in a :meth:`cursor.execute` call.
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Because of the way sessions interact with notifications (see |NOTIFY|_
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documentation), you should keep the connection in autocommit mode while
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sending and receiveng notification.
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.. |LISTEN| replace:: ``LISTEN``
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.. _LISTEN: http://www.postgresql.org/docs/8.4/static/sql-listen.html
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.. |NOTIFY| replace:: ``NOTIFY``
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.. _NOTIFY: http://www.postgresql.org/docs/8.4/static/sql-notify.html
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Example::
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import sys
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import select
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import psycopg2
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import psycopg2.extensions
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conn = psycopg2.connect(DSN)
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conn.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)
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curs = conn.cursor()
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curs.execute("LISTEN test;")
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print "Waiting for 'NOTIFY test'"
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while 1:
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if select.select([curs],[],[],5)==([],[],[]):
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print "Timeout"
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else:
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if curs.isready():
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print "Got NOTIFY:", curs.connection.notifies.pop()
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Running the script and executing the command ``NOTIFY test`` in a separate
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:program:`psql` shell, the output may look similar to::
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Waiting for 'NOTIFY test'
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Timeout
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Timeout
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Got NOTIFY: (6535, 'test')
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Timeout
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...
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.. index::
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double: Asynchronous; Query
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.. _asynchronous-queries:
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Asynchronous queries
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--------------------
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.. warning::
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Psycopg support for asynchronous queries is still experimental and the
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informations reported here may be out of date.
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Discussion testing and suggestions are welcome.
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Program code can initiate an asynchronous query by passing an ``async=1`` flag
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to the :meth:`cursor.execute` or :meth:`cursor.callproc` methods. A very
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simple example, from the connection to the query::
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conn = psycopg2.connect(database='test')
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curs = conn.cursor()
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curs.execute("SELECT * from test WHERE fielda > %s", (1971,), async=1)
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From then on any query on other cursors derived from the same connection is
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doomed to fail (and raise an exception) until the original cursor (the one
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executing the query) complete the asynchronous operation. This can happen in
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a number of different ways:
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1) one of the :obj:`.fetch*()` methods is called, effectively blocking until
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data has been sent from the backend to the client, terminating the query.
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2) :meth:`connection.cancel` is called. This method tries to abort the
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current query and will block until the query is aborted or fully executed.
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The return value is ``True`` if the query was successfully aborted or
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``False`` if it was executed. Query result are discarded in both cases.
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3) :meth:`cursor.execute` is called again on the same cursor
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(:obj:`.execute()` on a different cursor will simply raise an exception).
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This waits for the complete execution of the current query, discard any
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data and execute the new one.
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Note that calling :obj:`.execute()` two times in a row will not abort the
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former query and will temporarily go to synchronous mode until the first of
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the two queries is executed.
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Cursors now have some extra methods that make them useful during
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asynchronous queries:
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:meth:`cursor.fileno`
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Returns the file descriptor associated with the current connection and
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make possible to use a cursor in a context where a file object would be
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expected (like in a :func:`select()` call).
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:meth:`cursor.isready`
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Returns ``False`` if the backend is still processing the query or ``True``
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if data is ready to be fetched (by one of the :obj:`.fetch*()` methods).
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A code snippet that shows how to use the cursor object in a :func:`select()`
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call::
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import psycopg2
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import select
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conn = psycopg2.connect(database='test')
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curs = conn.cursor()
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curs.execute("SELECT * from test WHERE fielda > %s", (1971,), async=1)
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# wait for input with a maximum timeout of 5 seconds
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query_ended = False
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while not query_ended:
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rread, rwrite, rspec = select([curs, another_file], [], [], 5)
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if curs.isready():
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query_ended = True
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# manage input from other sources like other_file, etc.
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print "Query Results:"
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for row in curs:
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print row
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