pytest-postgresql

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:

   pip install pytest-postgresql

   You will also need to install psycopg (version 3). See its installation instructions.

   For async tests with psycopg.AsyncConnection, install the optional async extra:

   pip install pytest-postgresql[async]

   This installs:

   • pytest-asyncio (>= 1.4) — required for @pytest.mark.asyncio and postgresql_async fixtures.
   • aiofiles (>= 23.0) — required only when loading SQL files via the async loader (sql_async).

   On Windows, the plugin configures a SelectorEventLoop automatically for asyncio tests when no earlier pytest-asyncio loop factory is registered. This is required because psycopg async is incompatible with the default ProactorEventLoop on Windows (documented by psycopg). Without it, postgresql_async tests fail with Psycopg cannot use the 'ProactorEventLoop' to run in async mode. No extra configuration is needed when you install pytest-postgresql[async].

   With pytest-asyncio >= 1.4 on Windows, the plugin registers a selector loop factory via pytest-asyncio's loop-factory hook for all asyncio tests when no prior factory is provided. On Python 3.14+, the legacy asyncio policy fallback is not used because that API is deprecated.

   When an earlier hook implementation already supplies loop factories, those are preserved unchanged. Tests that use a prior factory may show different loop names in pytest IDs (for example test_example[custom] instead of test_example[selector]).

   If you use an older pytest-asyncio (< 1.4) on Windows with Python < 3.14, the plugin falls back to setting a global WindowsSelectorEventLoopPolicy for the entire test session — not only for postgresql async tests. That can change event-loop behaviour for unrelated asyncio tests in the same run. Install pytest-postgresql[async] (which pulls pytest-asyncio >= 1.4) to avoid that legacy path.

   pytest-asyncio configuration

   pytest-asyncio 1.x defaults to asyncio_mode = strict, so each async test must be marked with @pytest.mark.asyncio. If you set asyncio_mode = auto in pytest.ini or pyproject.toml, unmarked async test functions are detected automatically — postgresql_async still requires the [async] extra.

   [pytest]
   asyncio_mode = strict

   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.

   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()

   For async code, use postgresql_async with pytest.mark.asyncio:

   import pytest
   
   @pytest.mark.asyncio
   async def test_example_async(postgresql_async):
       """Check main async postgresql fixture."""
       async with postgresql_async.cursor() as cur:
           await cur.execute(
               "CREATE TABLE test (id serial PRIMARY KEY, num integer, data varchar);"
           )
           await postgresql_async.commit()

How to use

Warning

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

How does it work

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.
• postgresql_async - The async counterpart. It returns a connected psycopg.AsyncConnection. Requires pytest-postgresql[async] (pytest-asyncio >= 1.4), and each test must be marked with @pytest.mark.asyncio.

Async fixtures

postgresql_async and custom factories created with factories.postgresql_async are async generator fixtures using pytest_asyncio.fixture.

Minimum versions when installing manually instead of via [async]:

pytest-asyncio >= 1.4
aiofiles >= 23.0        # only for async SQL file loading

If pytest-asyncio is missing, fixture setup raises ImportError.

Async SQL file loading

Process and noproc fixtures always populate their template database synchronously during session setup (via DatabaseJanitor.load()), even when you use postgresql_async as the client fixture. SQL Path entries in a process fixture load list are executed with the sync sql() loader.

Use sql_async (requires aiofiles from the [async] extra) when you call AsyncDatabaseJanitor.load() directly with a Path. Callable loaders passed to AsyncDatabaseJanitor.load() may be sync or async; return values that are awaitable are awaited automatically.

from pathlib import Path
from pytest_postgresql import factories

postgresql_my_proc = factories.postgresql_proc(load=[Path("schema.sql")])
postgresql_my_async = factories.postgresql_async("postgresql_my_proc")

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:

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')

# Async client fixture (requires pytest-postgresql[async], pytest-asyncio >= 1.4)
postgresql_my_async = factories.postgresql_async('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:

1. Per test: In a client fixture, by using an intermediary fixture.
2. 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.

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:

pytest --postgresql-load=path/to/file.sql --postgresql-load=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.

For async tests against an external server, create a client fixture with factories.postgresql_async("postgresql_noproc") (see tests/examples/test_drop_test_database_async.py).

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.

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")

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

Configuration options

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)	tests
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 falls back to a common path like /usr/lib/postgresql/14/bin/pg_ctl.

Examples

Using SQLAlchemy

This example shows how to create an SQLAlchemy session fixture:

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 (pypi.org).

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

Advanced Usage: AsyncDatabaseJanitor

AsyncDatabaseJanitor is the async counterpart to DatabaseJanitor. Use it when managing database state with psycopg.AsyncConnection outside of standard fixtures. It requires psycopg (a core dependency). Install pytest-postgresql[async] when you need aiofiles for SQL file loading via sql_async, or pytest-asyncio for pytest async tests.

import pytest
import psycopg
from pytest_postgresql.janitor import AsyncDatabaseJanitor

@pytest.mark.asyncio
async def test_manual_async_janitor(postgresql_proc):
    async with AsyncDatabaseJanitor(
        user=postgresql_proc.user,
        host=postgresql_proc.host,
        port=postgresql_proc.port,
        dbname="my_custom_db",
        version=postgresql_proc.version,
        password="secret_password",
    ):
        async with await psycopg.AsyncConnection.connect(
            dbname="my_custom_db",
            user=postgresql_proc.user,
            host=postgresql_proc.host,
            port=postgresql_proc.port,
            password="secret_password",
        ) as conn:
            # use async connection
            pass

Connecting to PostgreSQL in Docker

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

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

In your tests:

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:

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:

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:

pipenv run tbump [NEW_VERSION]