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:

• The function connect() creates a new database session and returns a new Connection instance. AsyncConnection.connect() creates an asyncio connection instead.

• The Connection class encapsulates a database session. It allows to:

  • create new Cursor instances using the cursor() method to execute database commands and queries,

  • terminate transactions using the methods commit() or rollback() .

• The class Cursor allows interaction with the database:

  • send commands to the database using methods such as execute() and executemany() ,

  • retrieve data from the database, iterating on the cursor or using methods such as fetchone() , fetchmany() , fetchall() .

• 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 ).

Shortcuts #

The pattern above is familiar to psycopg2 users. However, Psycopg 3 also exposes a few simple extensions which make the above pattern leaner:

• the Connection objects exposes an execute() method, equivalent to creating a cursor, calling its execute() method, and returning it.

  # In Psycopg 2
  cur = conn.cursor()
  cur.execute(...)
  
  # In Psycopg 3
  cur = conn.execute(...)
  

• The Cursor.execute() method returns self . This means that you can chain a fetch operation, such as fetchone() , to the execute() call:

  # 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()[0])
# 2042-07-12 18:15:10.706497+01:00

Connection context #

Psycopg 3 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()

Note that, while the above pattern is what most people would use, connect() 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) commit() , rollback() , close() as and where needed.

AsyncConnection can be also used as context manager, using async with , but be careful about its quirkiness: see with async connections for details.

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 your program is concurrent and based on asyncio instead of on threads/processes, you can use async connections and cursors .

• If you want to customise the objects that the cursor returns, instead of receiving tuples, you can specify your row factories .

• If you want to customise how Python values and PostgreSQL types are mapped into each other, beside the basic type mapping , you can configure your types .