Psycopg exposes two new-style classes that can be sub-classed and expanded to adapt them to the needs of the programmer: psycopg2.extensions.cursor and psycopg2.extensions.connection. The connection class is usually sub-classed only to provide an easy way to create customized cursors but other uses are possible. cursor is much more interesting, because it is the class where query building, execution and result type-casting into Python variables happens.
An example of cursor subclass performing logging is:
import psycopg2
import psycopg2.extensions
import logging
class LoggingCursor(psycopg2.extensions.cursor):
def execute(self, sql, args=None):
logger = logging.getLogger('sql_debug')
logger.info(self.mogrify(sql, args))
try:
psycopg2.extensions.cursor.execute(self, sql, args)
except Exception, exc:
logger.error("%s: %s" % (exc.__class__.__name__, exc))
raise
conn = psycopg2.connect(DSN)
cur = conn.cursor(cursor_factory=LoggingCursor)
cur.execute("INSERT INTO mytable VALUES (%s, %s, %s);",
(10, 20, 30))
Any Python class or type can be adapted to an SQL string. Adaptation mechanism is similar to the Object Adaptation proposed in the PEP 246 and is exposed by the psycopg2.extensions.adapt() function.
The execute() method adapts its arguments to the ISQLQuote protocol. Objects that conform to this protocol expose a getquoted() method returning the SQL representation of the object as a string.
The easiest way to adapt an object to an SQL string is to register an adapter function via the register_adapter() function. The adapter function must take the value to be adapted as argument and return a conform object. A convenient object is the AsIs wrapper, whose getquoted() result is simply the str()ing conversion of the wrapped object.
Example: mapping of a Point class into the point PostgreSQL geometric type:
>>> from psycopg2.extensions import adapt, register_adapter, AsIs
>>> class Point(object):
... def __init__(self, x, y):
... self.x = x
... self.y = y
>>> def adapt_point(point):
... return AsIs("'(%s, %s)'" % (adapt(point.x), adapt(point.y)))
>>> register_adapter(Point, adapt_point)
>>> cur.execute("INSERT INTO atable (apoint) VALUES (%s)",
... (Point(1.23, 4.56),))
The above function call results in the SQL command:
INSERT INTO atable (apoint) VALUES ((1.23, 4.56));
PostgreSQL objects read from the database can be adapted to Python objects through an user-defined adapting function. An adapter function takes two arguments: the object string representation as returned by PostgreSQL and the cursor currently being read, and should return a new Python object. For example, the following function parses the PostgreSQL point representation into the previously defined Point class:
>>> def cast_point(value, cur):
... if value is None:
... return None
...
... # Convert from (f1, f2) syntax using a regular expression.
... m = re.match(r"\(([^)]+),([^)]+)\)", value)
... if m:
... return Point(float(m.group(1)), float(m.group(2)))
... else:
... raise InterfaceError("bad point representation: %r" % value)
In order to create a mapping from a PostgreSQL type (either standard or user-defined), its OID must be known. It can be retrieved either by the second column of the cursor.description:
>>> cur.execute("SELECT NULL::point")
>>> point_oid = cur.description[0][1]
>>> point_oid
600
or by querying the system catalog for the type name and namespace (the namespace for system objects is pg_catalog):
>>> cur.execute("""
... SELECT pg_type.oid
... FROM pg_type JOIN pg_namespace
... ON typnamespace = pg_namespace.oid
... WHERE typname = %(typename)s
... AND nspname = %(namespace)s""",
... {'typename': 'point', 'namespace': 'pg_catalog'})
>>> point_oid = cur.fetchone()[0]
>>> point_oid
600
After you know the object OID, you can create and register the new type:
>>> POINT = psycopg2.extensions.new_type((point_oid,), "POINT", cast_point)
>>> psycopg2.extensions.register_type(POINT)
The new_type() function binds the object OIDs (more than one can be specified) to the adapter function. register_type() completes the spell. Conversion is automatically performed when a column whose type is a registered OID is read:
>>> cur.execute("SELECT '(10.2,20.3)'::point")
>>> point = cur.fetchone()[0]
>>> print type(point), point.x, point.y
<class 'Point'> 10.2 20.3
Psycopg allows asynchronous interaction with other database sessions using the facilities offered by PostgreSQL commands LISTEN and NOTIFY. Please refer to the PostgreSQL documentation for examples of how to use this form of communications.
Notifications received are made available in the connection.notifies list. Notifications can be sent from Python code simply using a NOTIFY command in an execute() call.
Because of the way sessions interact with notifications (see NOTIFY documentation), you should keep the connection in autocommit mode if you wish to receive or send notifications in a timely manner.
Notification are received after every query execution. If the user is interested in receiving notification but not in performing any query, the poll() method can be used to check for notification without wasting resources.
A simple application could poll the connection from time to time to check if something new has arrived. A better strategy is to use some I/O completion function such as select() to sleep until awaken from the kernel when there is some data to read on the connection, thereby using no CPU unless there is something to read:
import select
import psycopg2
import psycopg2.extensions
conn = psycopg2.connect(DSN)
conn.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)
curs = conn.cursor()
curs.execute("LISTEN test;")
print "Waiting for 'NOTIFY test'"
while 1:
if select.select([conn],[],[],5) == ([],[],[]):
print "Timeout"
else:
conn.poll()
while conn.notifies:
print "Got NOTIFY:", conn.notifies.pop()
Running the script and executing the command NOTIFY test in a separate psql shell, the output may look similar to:
Waiting for 'NOTIFY test'
Timeout
Timeout
Got NOTIFY: (6535, 'test')
Timeout
...
New in version 2.2.0.
Psycopg can issue asynchronous queries to a PostgreSQL database. An asynchronous communication style is established passing the parameter async=1 to the connect() function: the returned connection will work in asynchronous mode.
In asynchronous mode, a Psycopg connection will rely on the caller to poll the socket file descriptor, checking if it is ready to accept data or if a query result has been transferred and is ready to be read on the client. The caller can use the method fileno() to get the connection file descriptor and poll() to make communication proceed according to the current connection state.
The following is an example loop using methods fileno() and poll() together with the Python select() function in order to carry on asynchronous operations with Psycopg:
def wait(conn):
while 1:
state = conn.poll()
if state == psycopg2.extensions.POLL_OK:
break
elif state == psycopg2.extensions.POLL_WRITE:
select.select([], [conn.fileno()], [])
elif state == psycopg2.extensions.POLL_READ:
select.select([conn.fileno()], [], [])
else:
raise psycopg2.OperationalError("poll() returned %s" % state)
The above loop of course would block an entire application: in a real asynchronous framework, select() would be called on many file descriptors waiting for any of them to be ready. Nonetheless the function can be used to connect to a PostgreSQL server only using nonblocking commands and the connection obtained can be used to perform further nonblocking queries. After poll() has returned POLL_OK, and thus wait() has returned, the connection can be safely used:
>>> aconn = psycopg2.connect(database='test', async=1)
>>> wait(aconn)
>>> acurs = aconn.cursor()
Notice that there are a few other requirements to be met in order to have a completely non-blocking connection attempt: see the libpq documentation for PQconnectStart().
The same loop should be also used to perform nonblocking queries: after sending a query via execute() or callproc(), call poll() on the connection available from cursor.connection until it returns POLL_OK, at which pont the query has been completely sent to the server and, if it produced data, the results have been transferred to the client and available using the regular cursor methods:
>>> acurs.execute("SELECT pg_sleep(5); SELECT 42;")
>>> wait(acurs.connection)
>>> acurs.fetchone()[0]
42
When an asynchronous query is being executed, connection.isexecuting() returns True. Two cursors can’t execute concurrent queries on the same asynchronous connection.
There are several limitations in using asynchronous connections: the connection is always in autocommit mode and it is not possible to change it using set_isolation_level(). So a transaction is not implicitly started at the first query and is not possible to use methods commit() and rollback(): you can manually control transactions using execute() to send database commands such as BEGIN, COMMIT and ROLLBACK.
With asynchronous connections it is also not possible to use set_client_encoding(), executemany(), large objects, named cursors.
COPY commands are not supported either in asynchronous mode, but this will be probably implemented in a future release.
New in version 2.2.0.
Psycopg can be used together with coroutine-based libraries, and participate to cooperative multithreading.
Coroutine-based libraries (such as Eventlet or gevent) can usually patch the Python standard library in order to enable a coroutine switch in the presence of blocking I/O: the process is usually referred as making the system green, in reference to the green threads.
Because Psycopg is a C extension module, it is not possible for coroutine libraries to patch it: Psycopg instead enables cooperative multithreading by allowing the registration of a wait callback using the psycopg2.extensions.set_wait_callback() function. When a wait callback is registered, Psycopg will use libpq non-blocking calls instead of the regular blocking ones, and will delegate to the callback the responsibility to wait for the socket to become readable or writable.
Working this way, the caller does not have the complete freedom to schedule the socket check whenever they want as with an asynchronous connection, but has the advantage of maintaining a complete DB API 2.0 semantics: from the point of view of the end user, all Psycopg functions and objects will work transparently in the coroutine environment (blocking the calling green thread and giving other green threads the possibility to be scheduled), allowing non modified code and third party libraries (such as SQLAlchemy) to be used in coroutine-based programs.
Notice that, while I/O correctly yields control to other coroutines, each connection has a lock allowing a single cursor at a time to communicate with the backend: such lock is not green, so blocking against it would block the entire program waiting for data, not the single coroutine. Therefore, programmers are advised to either avoid sharing connections between coroutines or to use a library-friendly lock to synchronize shared connections, e.g. for pooling.
Coroutine libraries authors should provide a callback implementation (and probably register it) to make Psycopg as green as they want. An example callback (using select() to block) is provided as psycopg2.extras.wait_select(): it boils down to something similar to:
def wait_select(conn):
while 1:
state = conn.poll()
if state == extensions.POLL_OK:
break
elif state == extensions.POLL_READ:
select.select([conn.fileno()], [], [])
elif state == extensions.POLL_WRITE:
select.select([], [conn.fileno()], [])
else:
raise OperationalError("bad state from poll: %s" % state)
Warning
COPY commands are currently not supported when a wait callback is registered, but they will be probably implemented in a future release.
Large objects are not supported either: they are not compatible with asynchronous connections.