Client-side cursors are what Psycopg uses in its normal querying process. They are implemented by the
AsyncCursor classes. In such querying pattern, after a cursor sends a query to the server (usually calling
execute()), the server replies transferring to the client the whole set of results requested, which is stored in the state of the same cursor and from where it can be read from Python code (using methods such as
fetchone() and siblings).
This querying process is very scalable because, after a query result has been transmitted to the client, the server doesn’t keep any state. Because the results are already in the client memory, iterating its rows is very quick.
The downside of this querying method is that the entire result has to be transmitted completely to the client (with a time proportional to its size) and the client needs enough memory to hold it, so it is only suitable for reasonably small result sets.
New in version 3.1.
The previously described client-side cursors send the query and the parameters separately to the server. This is the most efficient way to process parametrised queries and allows to build several features and optimizations. However, not all types of queries can be bound server-side; in particular no Data Definition Language query can. See Server-side binding for the description of these problems.
ClientCursor (and its
AsyncClientCursor async counterpart) merge the query on the client and send the query and the parameters merged together to the server. This allows to parametrize any type of PostgreSQL statement, not only queries (
SELECT) and Data Manipulation statements (
ClientCursor, Psycopg 3 behaviour will be more similar to
psycopg2 (which only implements client-side binding) and could be useful to port Psycopg 2 programs more easily to Psycopg 3. The objects in the
sql module allow for greater flexibility (for instance to parametrize a table name too, not only values); however, for simple cases, a
ClientCursor could be the right object.
In order to obtain
ClientCursor from a connection, you can set its
cursor_factory (at init time or changing its attribute afterwards):
from psycopg import connect, ClientCursor conn = psycopg.connect(DSN, cursor_factory=ClientCursor) cur = conn.cursor() # <psycopg.ClientCursor [no result] [IDLE] (database=piro) at 0x7fd977ae2880>
If you need to create a one-off client-side-binding cursor out of a normal connection, you can just use the
ClientCursor class passing the connection as argument.
conn = psycopg.connect(DSN) cur = psycopg.ClientCursor(conn)
The best use for client-side binding cursors is probably to port large Psycopg 2 code to Psycopg 3, especially for programs making wide use of Data Definition Language statements.
psycopg.sqlmodule allows for more generic client-side query composition, to mix client- and server-side parameters binding, and allows to parametrize tables and fields names too, or entirely generic SQL snippets.
PostgreSQL has its own concept of cursor too (sometimes also called portal). When a database cursor is created, the query is not necessarily completely processed: the server might be able to produce results only as they are needed. Only the results requested are transmitted to the client: if the query result is very large but the client only needs the first few records it is possible to transmit only them.
The downside is that the server needs to keep track of the partially processed results, so it uses more memory and resources on the server.
Psycopg allows the use of server-side cursors using the classes
AsyncServerCursor. They are usually created by passing the
name parameter to the
cursor() method (reason for which, in
psycopg2, they are usually called named cursors). The use of these classes is similar to their client-side counterparts: their interface is the same, but behind the scene they send commands to control the state of the cursor on the server (for instance when fetching new records or when moving using
Using a server-side cursor it is possible to process datasets larger than what would fit in the client’s memory. However for small queries they are less efficient because it takes more commands to receive their result, so you should use them only if you need to process huge results or if only a partial result is needed.
For instance if you have a PL/pgSQL function returning a cursor:
CREATE FUNCTION reffunc(refcursor) RETURNS refcursor AS $$ BEGIN OPEN $1 FOR SELECT col FROM test; RETURN $1; END; $$ LANGUAGE plpgsql;
you can run a one-off command in the same connection to call it (e.g. using
Connection.execute()) in order to create the cursor on the server:
after which you can create a server-side cursor declared by the same name, and directly call the fetch methods, skipping the
cur = conn.cursor('curname') # no cur.execute() for record in cur: # or cur.fetchone(), cur.fetchmany()... # do something with record