Basic module usage#
The basic Psycopg usage is common to all the database adapters implementing
the DB-API protocol. Other database adapters, such as the builtin
psycopg2, have roughly the same pattern of interaction.
Main objects in Psycopg 3#
Here is an interactive session showing some of the basic commands:
# Note: the module name is psycopg, not psycopg3 import psycopg # Connect to an existing database with psycopg.connect("dbname=test user=postgres") as conn: # Open a cursor to perform database operations with conn.cursor() as cur: # Execute a command: this creates a new table cur.execute(""" CREATE TABLE test ( id serial PRIMARY KEY, num integer, data text) """) # Pass data to fill a query placeholders and let Psycopg perform # the correct conversion (no SQL injections!) cur.execute( "INSERT INTO test (num, data) VALUES (%s, %s)", (100, "abc'def")) # Query the database and obtain data as Python objects. cur.execute("SELECT * FROM test") cur.fetchone() # will return (1, 100, "abc'def") # You can use `cur.fetchmany()`, `cur.fetchall()` to return a list # of several records, or even iterate on the cursor for record in cur: print(record) # Make the changes to the database persistent conn.commit()
In the example you can see some of the main objects and methods and how they relate to each other:
Connectionclass encapsulates a database session. It allows to:
Cursorallows interaction with the database:
Using these objects as context managers (i.e. using
with) will make sure to close them and free their resources at the end of the block (notice that this is different from psycopg2).
The pattern above is familiar to
psycopg2 users. However, Psycopg 3 also
exposes a few simple extensions which make the above pattern leaner:
# In Psycopg 2 cur = conn.cursor() cur.execute(...) # In Psycopg 3 cur = conn.execute(...)
# In Psycopg 2 cur.execute(...) record = cur.fetchone() cur.execute(...) for record in cur: ... # In Psycopg 3 record = cur.execute(...).fetchone() for record in cur.execute(...): ...
Using them together, in simple cases, you can go from creating a connection to using a result in a single expression:
print(psycopg.connect(DSN).execute("SELECT now()").fetchone()) # 2042-07-12 18:15:10.706497+01:00
Connection can be used as a context manager:
with psycopg.connect() as conn: ... # use the connection # the connection is now closed
When the block is exited, if there is a transaction open, it will be committed. If an exception is raised within the block the transaction is rolled back. In both cases the connection is closed. It is roughly the equivalent of:
conn = psycopg.connect() try: ... # use the connection except BaseException: conn.rollback() else: conn.commit() finally: conn.close()
This behaviour is not what
psycopg2 does: in
psycopg2 there is
no final close() and the connection can be used in several
with statements to manage different transactions. This behaviour has
been considered non-standard and surprising so it has been replaced by the
Note that, while the above pattern is what most people would use,
doesn’t enter a block itself, but returns an “un-entered” connection, so that
it is still possible to use a connection regardless of the code scope and the
developer is free to use (and responsible for calling)
close() as and where needed.
If a connection is just left to go out of scope, the way it will behave
with or without the use of a
with block is different:
if the connection is used without a
withblock, the server will find a connection closed INTRANS and roll back the current transaction;
if the connection is used with a
withblock, there will be an explicit COMMIT and the operations will be finalised.
You should use a
with block when your intention is just to execute a
set of operations and then committing the result, which is the most usual
thing to do with a connection. If your connection life cycle and
transaction pattern is different, and want more control on it, the use
with might be more convenient.
See Transactions management for more information.
Adapting pyscopg to your program#
The above pattern of use only shows the default behaviour of the adapter. Psycopg can be customised in several ways, to allow the smoothest integration between your Python program and your PostgreSQL database:
If you want to customise the objects that the cursor returns, instead of receiving tuples, you can specify your row factories.