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.

   deftest_example(postgresql):
   """Check main postgresql fixture."""withpostgresql.cursor() ascur:
   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:

   importpytest@pytest.mark.asyncioasyncdeftest_example_async(postgresql_async):
   """Check main async postgresql fixture."""asyncwithpostgresql_async.cursor() ascur:
   awaitcur.execute(
   "CREATE TABLE test (id serial PRIMARY KEY, num integer, data varchar);"
   )
   awaitpostgresql_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.

frompathlibimportPathfrompytest_postgresqlimportfactoriespostgresql_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:

frompytest_postgresqlimportfactories# Create a custom process fixturepostgresql_my_proc=factories.postgresql_proc(
port=None, unixsocketdir='/var/run')
# Create a client fixture that uses the custom processpostgresql_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.

frompathlibimportPathpostgresql_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

If a loaded .sql file contains statements that cannot run inside a transaction block (for example CREATE DATABASE), enable autocommit on the loader connection. This can be set without writing a custom loader, via the factory argument, the command line, or pytest.ini:

postgresql_my_proc=factories.postgresql_proc(load=[Path("with_create_db.sql")], load_autocommit=True)

pytest --postgresql-load-autocommit

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.

Using a maintenance database other than postgres

Creating and dropping the test databases requires a connection to a database that already exists - by default postgres. If the test role merely lacks the privilege, GRANT CONNECT ON DATABASE postgres TO myuser is the simpler fix. When postgres is not reachable at all - behind a connection pooler with a fixed database list, or on a managed server that does not expose it - point the noproc fixture at another database:

pytest --postgresql-maintenance-dbname=my_existing_db

postgresql_external=factories.postgresql_noproc(maintenance_dbname="my_existing_db")

The database is only connected to, never created, modified or dropped. Avoid template1: CREATE DATABASE clones it when no template is given, and PostgreSQL does not expect the source database to be in use while it is being copied.

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.

frompytest_postgresqlimportfactories# 1. Start with a process or a no-process basebase_proc=factories.postgresql_proc(load=[load_schema])
# 2. Add a layer with some dataseeded_noproc=factories.postgresql_noproc(depends_on="base_proc", load=[load_data])
# 3. Add another layer with more datamore_seeded_noproc=factories.postgresql_noproc(depends_on="seeded_noproc", load=[load_more_data])
# 4. Use the final layer in your testclient=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

Setting	postgresql_proc argument	postgresql_noproc argument	Command line option	pytest.ini option	Default
Path to executable	executable	n/a	--postgresql-exec	postgresql_exec	pg_config --bindir + pg_ctl
host	host	host	--postgresql-host	postgresql_host	127.0.0.1
port	port	port	--postgresql-port	postgresql_port	random (proc), 5432 (noproc)
Port search count	—	n/a	--postgresql-port-search-count	postgresql_port_search_count	5
postgresql user	user	user	--postgresql-user	postgresql_user	postgres
password	password	password	--postgresql-password	postgresql_password
Starting parameters (extra pg_ctl arguments)	startparams	n/a	--postgresql-startparams	postgresql_startparams	-w
Postgres exe extra arguments (passed via pg_ctl's -o argument)	postgres_options	n/a	--postgresql-postgres-options	postgresql_postgres_options
Location for unixsockets	unixsocket	n/a	--postgresql-unixsocketdir	postgresql_unixsocketdir	$TMPDIR
Database name	dbname	dbname (handles xdist)	--postgresql-dbname	postgresql_dbname	tests
Maintenance database name	n/a	maintenance_dbname	--postgresql-maintenance-dbname	postgresql_maintenance_dbname	postgres
Default Schema (load list)	load	load	--postgresql-load	postgresql_load
Autocommit for the SQL loader connection	load_autocommit	load_autocommit	--postgresql-load-autocommit	postgresql_load_autocommit	False
PostgreSQL connection options	options	options	--postgresql-options	postgresql_options
Drop test database on start	—	—	--postgresql-drop-test-database		false
Fixture to layer the template database on	n/a	depends_on

— means the setting applies to that fixture but has no factory argument; n/a means the setting does not apply to that fixture at all.

Note

If the executable factory argument is not provided, the plugin looks for pg_ctl first at the configured path (--postgresql-exec / the postgresql_exec ini option, /usr/lib/postgresql/14/bin/pg_ctl by default), then in the directory reported by pg_config --bindir.

Only the executable factory argument is taken at face value; the other two are verified to exist before being used. If neither is there, an ExecutableMissingException is raised listing the locations that were checked.

Note

postgresql_proc starts and manages a PostgreSQL server for you, so it needs the PostgreSQL server installed locally. Installing only the client libraries (libpq-dev on Debian/Ubuntu) provides pg_config but no pg_ctl, and the plugin will refuse to start. To test against a server you run yourself — a dockerised one, for instance — use the postgresql_noproc fixture instead.

Examples

Using SQLAlchemy

This example shows how to create an SQLAlchemy session fixture:

fromtypingimportIteratorimportpytestfrompsycopgimportConnectionfromsqlalchemyimportcreate_enginefromsqlalchemy.ormimportSession, sessionmaker, scoped_sessionfromsqlalchemy.poolimportNullPool@pytest.fixturedefdb_session(postgresql: Connection) ->Iterator[Session]:
"""Session for SQLAlchemy."""user=postgresql.info.userhost=postgresql.info.hostport=postgresql.info.portdbname=postgresql.info.dbnameconnection_str=f'postgresql+psycopg://{user}:@{host}:{port}/{dbname}'engine=create_engine(connection_str, echo=False, poolclass=NullPool)
# Assuming you use a Base modelfrommy_app.modelsimportBaseBase.metadata.create_all(engine)
SessionLocal=scoped_session(sessionmaker(bind=engine))
yieldSessionLocal()
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).

importpsycopgfrompytest_postgresql.janitorimportDatabaseJanitordeftest_manual_janitor(postgresql_proc):
withDatabaseJanitor(
user=postgresql_proc.user,
host=postgresql_proc.host,
port=postgresql_proc.port,
dbname="my_custom_db",
version=postgresql_proc.version,
password="secret_password",
):
withpsycopg.connect(
dbname="my_custom_db",
user=postgresql_proc.user,
host=postgresql_proc.host,
port=postgresql_proc.port,
password="secret_password",
) asconn:
# use connectionpass

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.

importpytestimportpsycopgfrompytest_postgresql.janitorimportAsyncDatabaseJanitor@pytest.mark.asyncioasyncdeftest_manual_async_janitor(postgresql_proc):
asyncwithAsyncDatabaseJanitor(
user=postgresql_proc.user,
host=postgresql_proc.host,
port=postgresql_proc.port,
dbname="my_custom_db",
version=postgresql_proc.version,
password="secret_password",
):
asyncwithawaitpsycopg.AsyncConnection.connect(
dbname="my_custom_db",
user=postgresql_proc.user,
host=postgresql_proc.host,
port=postgresql_proc.port,
password="secret_password",
) asconn:
# use async connectionpass

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:

frompytest_postgresqlimportfactoriespostgresql_in_docker=factories.postgresql_noproc()
postgresql=factories.postgresql("postgresql_in_docker", dbname="test")
deftest_docker(postgresql):
withpostgresql.cursor() ascur:
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:

importpsycopgfrompytest_postgresqlimportfactoriesdefload_database(**kwargs):
withpsycopg.connect(**kwargs) asconn:
withconn.cursor() ascur:
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")
deftest_stories(postgresql):
withpostgresql.cursor() ascur:
cur.execute("SELECT count(*) FROM stories")
assertcur.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.