.. currentmodule:: psycopg

.. _static-typing:

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.

.. _Mypy: http://mypy-lang.org/


Generic types
-------------

Psycopg `Connection` and `Cursor` objects are `~typing.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 `~Connection.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]`.

.. code:: python

   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, ...]]


.. _row-factory-static:

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 `~Connection.connect()` and the `~Connection.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
:ref:`row-factories` for more details.

.. code:: python

   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]]


.. _pool-generic:

Generic pool types
------------------

.. versionadded:: 3.2

The `~psycopg_pool.ConnectionPool` class and similar are generic on their
`!connection_class` argument. The `~psycopg_pool.ConnectionPool.connection()`
method is annotated as returning a connection of that type, and the record
returned will follow the rule as in :ref:`row-factory-static`.

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.

.. code:: python

    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`:

.. code:: python

    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-pydantic:

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.

.. _Pydantic: https://pydantic-docs.helpmanual.io/

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.

.. code:: python

    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")


.. _literal-string:

Checking literal strings in queries
-----------------------------------

The `~Cursor.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:

.. code:: python

    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:

.. code:: python

    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.

.. __: https://github.com/python/mypy/issues/12554
.. __: https://github.com/facebook/pyre-check/issues/636

.. _Pyre: https://pyre-check.org/