pytest-postgresql

Postgresql fixtures and fixture factories for Pytest.

47 个版本 Python >=3.10
安装
pip install pytest-postgresql
poetry add pytest-postgresql
pipenv install pytest-postgresql
conda install pytest-postgresql
描述

.. image:: https://raw.githubusercontent.com/dbfixtures/pytest-postgresql/main/logo.png :width: 100px :height: 100px

pytest-postgresql

.. image:: https://img.shields.io/pypi/v/pytest-postgresql.svg :target: https://pypi.python.org/pypi/pytest-postgresql/ :alt: Latest PyPI version

.. image:: https://img.shields.io/pypi/wheel/pytest-postgresql.svg :target: https://pypi.python.org/pypi/pytest-postgresql/ :alt: Wheel Status

.. image:: https://img.shields.io/pypi/pyversions/pytest-postgresql.svg :target: https://pypi.python.org/pypi/pytest-postgresql/ :alt: Supported Python Versions

.. image:: https://img.shields.io/pypi/l/pytest-postgresql.svg :target: https://pypi.python.org/pypi/pytest-postgresql/ :alt: License

What is this?

This is a pytest plugin that enables you to test code relying on a running PostgreSQL database. It provides fixtures for managing both the PostgreSQL process and the client connections.

Quick Start

  1. Install the plugin:

    .. code-block:: sh

    pip install pytest-postgresql
    

    You will also need to install psycopg (version 3). See its installation instructions <https://www.psycopg.org/psycopg3/docs/basic/install.html>_.

    .. note::

    While this plugin requires ``psycopg`` 3 to manage the database, your application code can still use ``psycopg`` 2.
    
  2. Run a test:

    Simply include the postgresql fixture in your test. It provides a connected psycopg.Connection object.

    .. code-block:: python

    def test_example(postgresql):
        """Check main postgresql fixture."""
        with postgresql.cursor() as cur:
            cur.execute("CREATE TABLE test (id serial PRIMARY KEY, num integer, data varchar);")
            postgresql.commit()
    

How to use

.. warning::

Tested on PostgreSQL versions >= 14. See tests for more details.

How does it work

.. image:: https://raw.githubusercontent.com/dbfixtures/pytest-postgresql/main/docs/images/architecture.svg :alt: Project Architecture Diagram :align: center

The plugin provides two main types of fixtures:

1. Client Fixtures These provide a connection to a database for your tests.

* **postgresql** - A function-scoped fixture. It returns a connected ``psycopg.Connection``.
  After each test, it terminates leftover connections and drops the test database to ensure isolation.

2. Process Fixtures These manage the PostgreSQL server lifecycle.

* **postgresql_proc** - A session-scoped fixture that starts a PostgreSQL instance on its first use and stops it when all tests are finished.
* **postgresql_noproc** - A fixture for connecting to an already running PostgreSQL instance (e.g., in Docker or CI).

Customizing Fixtures

You can create additional fixtures using factories:

.. code-block:: python

from pytest_postgresql import factories

# Create a custom process fixture
postgresql_my_proc = factories.postgresql_proc(
    port=None, unixsocketdir='/var/run')

# Create a client fixture that uses the custom process
postgresql_my = factories.postgresql('postgresql_my_proc')

.. note::

Each process fixture can be configured independently through factory arguments.

Pre-populating the database for tests

If you want the database to be automatically pre-populated with your schema and data, there are two levels you can achieve it:

#. Per test: In a client fixture, by using an intermediary fixture. #. Per session: In a process fixture.

The process fixture accepts a load parameter, which supports:

  • SQL file paths: Loads and executes the SQL files.
  • Loading functions: A callable or an import string (e.g., "path.to.module:function"). These functions receive host, port, user, dbname, and password and must perform the connection themselves (or use an ORM).

The process fixture pre-populates the database once per session into a template database. The client fixture then clones this template for each test, which significantly speeds up your tests.

.. code-block:: python

from pathlib import Path
postgresql_my_proc = factories.postgresql_proc(
    load=[
        Path("schemafile.sql"),
        "import.path.to.function",
        load_this_callable
    ]
)

Defining pre-population on the command line:

.. code-block:: sh

pytest --postgresql-populate-template=path/to/file.sql --postgresql-populate-template=path.to.function

Connecting to an existing PostgreSQL database

To connect to an external server (e.g., running in Docker), use the postgresql_noproc fixture.

.. code-block:: python

postgresql_external = factories.postgresql('postgresql_noproc')

By default, it connects to 127.0.0.1:5432.

Chaining fixtures

You can chain multiple postgresql_noproc fixtures to layer your data pre-population. Each fixture in the chain will create its own template database based on the previous one.

.. code-block:: python

from pytest_postgresql import factories

# 1. Start with a process or a no-process base
base_proc = factories.postgresql_proc(load=[load_schema])

# 2. Add a layer with some data
seeded_noproc = factories.postgresql_noproc(depends_on="base_proc", load=[load_data])

# 3. Add another layer with more data
more_seeded_noproc = factories.postgresql_noproc(depends_on="seeded_noproc", load=[load_more_data])

# 4. Use the final layer in your test
client = factories.postgresql("more_seeded_noproc")

.. image:: https://raw.githubusercontent.com/dbfixtures/pytest-postgresql/main/docs/images/architecture_chaining.svg :alt: Fixture Chaining Diagram :align: center

Configuration

You can define settings via fixture factory arguments, command line options, or pytest.ini. They are resolved in this order:

  1. Fixture factory argument
  2. Command line option
  3. pytest.ini configuration option

.. list-table:: Configuration options :header-rows: 1

    • PostgreSQL option
    • Fixture factory argument
    • Command line option
    • pytest.ini option
    • Noop process fixture
    • Default
    • Path to executable
    • executable
    • --postgresql-exec
    • postgresql_exec
    • pg_config --bindir + pg_ctl
    • host
    • host
    • --postgresql-host
    • postgresql_host
    • yes
    • 127.0.0.1
    • port
    • port
    • --postgresql-port
    • postgresql_port
    • yes (5432)
    • random
    • Port search count
    • --postgresql-port-search-count
    • postgresql_port_search_count
    • 5
    • postgresql user
    • user
    • --postgresql-user
    • postgresql_user
    • yes
    • postgres
    • password
    • password
    • --postgresql-password
    • postgresql_password
    • yes
    • Starting parameters (extra pg_ctl arguments)
    • startparams
    • --postgresql-startparams
    • postgresql_startparams
    • -w
    • Postgres exe extra arguments (passed via pg_ctl's -o argument)
    • postgres_options
    • --postgresql-postgres-options
    • postgresql_postgres_options
    • Location for unixsockets
    • unixsocket
    • --postgresql-unixsocketdir
    • postgresql_unixsocketdir
    • $TMPDIR
    • Database name
    • dbname
    • --postgresql-dbname
    • postgresql_dbname
    • yes (handles xdist)
    • test
    • Default Schema (load list)
    • load
    • --postgresql-load
    • postgresql_load
    • yes
    • PostgreSQL connection options
    • options
    • --postgresql-options
    • postgresql_options
    • yes
    • Drop test database on start
    • --postgresql-drop-test-database
    • false

.. note::

If the ``executable`` is not provided, the plugin attempts to find it by calling ``pg_config``. If that fails, it fallbacks to a common path like ``/usr/lib/postgresql/13/bin/pg_ctl``.

Examples

Using SQLAlchemy

This example shows how to create an SQLAlchemy session fixture:

.. code-block:: python

from typing import Iterator
import pytest
from psycopg import Connection
from sqlalchemy import create_engine
from sqlalchemy.orm import Session, sessionmaker, scoped_session
from sqlalchemy.pool import NullPool

@pytest.fixture
def db_session(postgresql: Connection) -> Iterator[Session]:
    """Session for SQLAlchemy."""
    user = postgresql.info.user
    host = postgresql.info.host
    port = postgresql.info.port
    dbname = postgresql.info.dbname

    connection_str = f'postgresql+psycopg://{user}:@{host}:{port}/{dbname}'
    engine = create_engine(connection_str, echo=False, poolclass=NullPool)

    # Assuming you use a Base model
    from my_app.models import Base
    Base.metadata.create_all(engine)

    SessionLocal = scoped_session(sessionmaker(bind=engine))
    yield SessionLocal()

    SessionLocal.close()
    Base.metadata.drop_all(engine)

Advanced Usage: DatabaseJanitor

DatabaseJanitor is an advanced API for managing database state outside of standard fixtures. It is used by projects like Warehouse <https://github.com/pypa/warehouse>_ (pypi.org).

.. code-block:: python

import psycopg
from pytest_postgresql.janitor import DatabaseJanitor

def test_manual_janitor(postgresql_proc):
    with DatabaseJanitor(
        user=postgresql_proc.user,
        host=postgresql_proc.host,
        port=postgresql_proc.port,
        dbname="my_custom_db",
        version=postgresql_proc.version,
        password="secret_password",
    ):
        with psycopg.connect(
            dbname="my_custom_db",
            user=postgresql_proc.user,
            host=postgresql_proc.host,
            port=postgresql_proc.port,
            password="secret_password",
        ) as conn:
            # use connection
            pass

Connecting to PostgreSQL in Docker

To connect to a Docker-run PostgreSQL, use the noproc fixture.

.. code-block:: sh

docker run --name some-postgres -e POSTGRES_PASSWORD=mysecret -d postgres

In your tests:

.. code-block:: python

from pytest_postgresql import factories

postgresql_in_docker = factories.postgresql_noproc()
postgresql = factories.postgresql("postgresql_in_docker", dbname="test")

def test_docker(postgresql):
    with postgresql.cursor() as cur:
        cur.execute("SELECT 1")

Run with:

.. code-block:: sh

pytest --postgresql-host=172.17.0.2 --postgresql-password=mysecret

Basic database state for all tests

You can define a load function and pass it to your process fixture factory:

.. code-block:: python

import psycopg
from pytest_postgresql import factories

def load_database(**kwargs):
    with psycopg.connect(**kwargs) as conn:
        with conn.cursor() as cur:
            cur.execute("CREATE TABLE stories (id serial PRIMARY KEY, name varchar);")
            cur.execute("INSERT INTO stories (name) VALUES ('Silmarillion'), ('The Expanse');")

postgresql_proc = factories.postgresql_proc(load=[load_database])
postgresql = factories.postgresql("postgresql_proc")

def test_stories(postgresql):
    with postgresql.cursor() as cur:
        cur.execute("SELECT count(*) FROM stories")
        assert cur.fetchone()[0] == 2

The process fixture populates the template database once, and the client fixture clones it for every test. This is fast, clean, and ensures no dangling transactions. This approach works with both postgresql_proc and postgresql_noproc.

Release

Install pipenv and dev dependencies, then run:

.. code-block:: sh

pipenv run tbump [NEW_VERSION]