Static Typing

Psycopg source code is annotated according to PEP 0484 type hints and is checked using the current version of Mypy in --strict mode.

If your application is checked using Mypy too you can make use of Psycopg types to validate the correct use of Psycopg objects and of the data returned by the database.

Generic types

Psycopg Connection and Cursor objects are Generic objects and support a Row parameter which is the type of the records returned. The parameter can be configured by passing a row_factory parameter to the constructor or to the cursor() method.

By default, methods producing records such as Cursor.fetchall() return normal tuples of unknown size and content. As such, the connect() function returns an object of type psycopg.Connection[tuple[Any, ...]] and Connection.cursor() returns an object of type psycopg.Cursor[tuple[Any, ...]]. If you are writing generic plumbing code it might be practical to use annotations such as Connection[Any] and Cursor[Any].

conn = psycopg.connect() # type is psycopg.Connection[tuple[Any, ...]]

cur = conn.cursor()      # type is psycopg.Cursor[tuple[Any, ...]]

rec = cur.fetchone()     # type is Optional[tuple[Any, ...]]

recs = cur.fetchall()    # type is List[tuple[Any, ...]]

Type of rows returned

If you want to use connections and cursors returning your data as different types, for instance as dictionaries, you can use the row_factory argument of the connect() and the cursor() method, which will control what type of record is returned by the fetch methods of the cursors and annotate the returned objects accordingly. See Row factories for more details.

dconn = psycopg.connect(row_factory=dict_row)
# dconn type is psycopg.Connection[dict[str, Any]]

dcur = conn.cursor(row_factory=dict_row)
dcur = dconn.cursor()
# dcur type is psycopg.Cursor[dict[str, Any]] in both cases

drec = dcur.fetchone()
# drec type is Optional[dict[str, Any]]

Generic pool types

New in version 3.2.

The ConnectionPool class and similar are generic on their connection_class argument. The connection() method is annotated as returning a connection of that type, and the record returned will follow the rule as in Type of rows returned.

Note that, at the moment, if you use a generic class as connection_class, you will need to specify a row_factory consistently in the kwargs, otherwise the typing system and the runtime will not agree.

from psycopg import Connection
from psycopg.rows import DictRow, dict_row

with ConnectionPool(
    connection_class=Connection[DictRow],   # provides type hinting
    kwargs={"row_factory": dict_row},       # works at runtime
) as pool:
    # reveal_type(pool): ConnectionPool[Connection[dict[str, Any]]]

    with pool.connection() as conn:
        # reveal_type(conn): Connection[dict[str, Any]]

        row = conn.execute("SELECT now()").fetchone()
        # reveal_type(row): Optional[dict[str, Any]]

        print(row)  # {"now": datetime.datetime(...)}

If a non-generic Connection subclass is used (one whose returned row type is not parametric) then it’s not necessary to specify kwargs:

class MyConnection(Connection[DictRow]):
    def __init__(self, *args, **kwargs):
        kwargs["row_factory"] = dict_row
        super().__init__(*args, **kwargs)

with ConnectionPool(connection_class=MyConnection) as pool:
    # reveal_type(pool): ConnectionPool[MyConnection]

    with pool.connection() as conn:
        # reveal_type(conn): MyConnection

        row = conn.execute("SELECT now()").fetchone()
        # reveal_type(row): Optional[dict[str, Any]]

        print(row)  # {"now": datetime.datetime(...)}

Example: returning records as Pydantic models

Using Pydantic it is possible to enforce static typing at runtime. Using a Pydantic model factory the code can be checked statically using Mypy and querying the database will raise an exception if the rows returned is not compatible with the model.

The following example can be checked with mypy --strict without reporting any issue. Pydantic will also raise a runtime error in case the Person is used with a query that returns incompatible data.

from datetime import date
from typing import Optional

import psycopg
from psycopg.rows import class_row
from pydantic import BaseModel

class Person(BaseModel):
    id: int
    first_name: str
    last_name: str
    dob: Optional[date]

def fetch_person(id: int) -> Person:
    with psycopg.connect() as conn:
        with conn.cursor(row_factory=class_row(Person)) as cur:
            cur.execute(
                """
                SELECT id, first_name, last_name, dob
                FROM (VALUES
                    (1, 'John', 'Doe', '2000-01-01'::date),
                    (2, 'Jane', 'White', NULL)
                ) AS data (id, first_name, last_name, dob)
                WHERE id = %(id)s;
                """,
                {"id": id},
            )
            obj = cur.fetchone()

            # reveal_type(obj) would return 'Optional[Person]' here

            if not obj:
                raise KeyError(f"person {id} not found")

            # reveal_type(obj) would return 'Person' here

            return obj

for id in [1, 2]:
    p = fetch_person(id)
    if p.dob:
        print(f"{p.first_name} was born in {p.dob.year}")
    else:
        print(f"Who knows when {p.first_name} was born")

Checking literal strings in queries

The execute() method and similar should only receive a literal string as input, according to PEP 675. This means that the query should come from a literal string in your code, not from an arbitrary string expression.

For instance, passing an argument to the query should be done via the second argument to execute(), not by string composition:

def get_record(conn: psycopg.Connection[Any], id: int) -> Any:
    cur = conn.execute("SELECT * FROM my_table WHERE id = %s" % id)  # BAD!
    return cur.fetchone()

# the function should be implemented as:

def get_record(conn: psycopg.Connection[Any], id: int) -> Any:
    cur = conn.execute("select * FROM my_table WHERE id = %s", (id,))
    return cur.fetchone()

If you are composing a query dynamically you should use the sql.SQL object and similar to escape safely table and field names. The parameter of the SQL() object should be a literal string:

def count_records(conn: psycopg.Connection[Any], table: str) -> int:
    query = "SELECT count(*) FROM %s" % table  # BAD!
    return conn.execute(query).fetchone()[0]

# the function should be implemented as:

def count_records(conn: psycopg.Connection[Any], table: str) -> int:
    query = sql.SQL("SELECT count(*) FROM {}").format(sql.Identifier(table))
    return conn.execute(query).fetchone()[0]

At the time of writing, no Python static analyzer implements this check (mypy doesn’t implement it, Pyre does, but doesn’t work with psycopg yet). Once the type checkers support will be complete, the above bad statements should be reported as errors.