A pytest plugin, that enables you to test your code that relies on a running PostgreSQL Database

Overview

https://raw.githubusercontent.com/ClearcodeHQ/pytest-postgresql/master/logo.png

pytest-postgresql

Latest PyPI version Wheel Status Supported Python Versions License

What is this?

This is a pytest plugin, that enables you to test your code that relies on a running PostgreSQL Database. It allows you to specify fixtures for PostgreSQL process and client.

How to use

Warning

Tested on PostgreSQL versions >= 10. See tests for more details.

Install with:

pip install pytest-postgresql

You will also need to install psycopg. See its installation instructions.

Plugin contains three fixtures:

  • postgresql - it's a client fixture that has functional scope. After each test it ends all leftover connections, and drops test database from PostgreSQL ensuring repeatability. This fixture returns already connected psycopg connection.
  • postgresql_proc - session scoped fixture, that starts PostgreSQL instance at it's first use and stops at the end of the tests.
  • postgresql_noproc - a noprocess fixture, that's connecting to already running postgresql instance. For example on dockerized test environments, or CI providing postgresql services

Simply include one of these fixtures into your tests fixture list.

You can also create additional postgresql client and process fixtures if you'd need to:

from pytest_postgresql import factories

postgresql_my_proc = factories.postgresql_proc(
    port=None, unixsocketdir='/var/run')
postgresql_my = factories.postgresql('postgresql_my_proc')

Note

Each PostgreSQL process fixture can be configured in a different way than the others through the fixture factory arguments.

Sample test

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

If you want the database fixture to be automatically populated with your schema there are two ways:

  1. client fixture specific
  2. process fixture specific

Both are accepting same set of possible loaders:

  • sql file path
  • loading function import path (string)
  • actual loading function

That function will receive host, port, user, dbname and password kwargs and will have to perform connection to the database inside. However, you'll be able to run SQL files or even trigger programmatically database migrations you have.

Client specific loads the database each test

postgresql_my_with_schema = factories.postgresql(
    'postgresql_my_proc',
    load=["schemafile.sql", "otherschema.sql", "import.path.to.function", "import.path.to:otherfunction", load_this]
)

Warning

This way, the database will still be dropped each time.

The process fixture performs the load once per test session, and loads the data into the template database. Client fixture then creates test database out of the template database each test, which significantly speeds up the tests.

postgresql_my_proc = factories.postgresql_proc(
    load=["schemafile.sql", "otherschema.sql", "import.path.to.function", "import.path.to:otherfunction", load_this]
)
pytest --postgresql-populate-template=path.to.loading_function --postgresql-populate-template=path.to.other:loading_function --postgresql-populate-template=path/to/file.sql

The loading_function from example will receive , and have to commit that. Connecting to already existing postgresql database --------------------------------------------------

Some projects are using already running postgresql servers (ie on docker instances). In order to connect to them, one would be using the postgresql_noproc fixture.

postgresql_external = factories.postgresql('postgresql_noproc')

By default the postgresql_noproc fixture would connect to postgresql instance using 5432 port. Standard configuration options apply to it.

These are the configuration options that are working on all levels with the postgresql_noproc fixture:

Configuration

You can define your settings in three ways, it's fixture factory argument, command line option and pytest.ini configuration option. You can pick which you prefer, but remember that these settings are handled in the following order:

  • Fixture factory argument
  • Command line option
  • Configuration option in your pytest.ini file
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
/usr/lib/postgresql/13/bin/pg_ctl
host host --postgresql-host postgresql_host yes 127.0.0.1
port port --postgresql-port postgresql_port yes (5432) random
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
 
Log filename's prefix logsprefix --postgresql-logsprefix postgresql_logsprefix
 
Location for unixsockets unixsocket --postgresql-unixsocketdir postgresql_unixsocketdir
$TMPDIR
Database name dbname --postgresql-dbname postgresql_dbname yes, however with xdist an index is being added to name, resulting in test0, test1 for each worker. test
Default Schema either in sql files or import path to function that will load it (list of values for each) load --postgresql-load postgresql_load yes  
PostgreSQL connection options options --postgresql-options postgresql_options yes  

Example usage:

  • pass it as an argument in your own fixture

    postgresql_proc = factories.postgresql_proc(
        port=8888)
  • use --postgresql-port command line option when you run your tests

    py.test tests --postgresql-port=8888
    
  • specify your port as postgresql_port in your pytest.ini file.

    To do so, put a line like the following under the [pytest] section of your pytest.ini:

    [pytest]
    postgresql_port = 8888

Examples

Populating database for tests

With SQLAlchemy

This example shows how to populate database and create an SQLAlchemy's ORM connection:

Sample below is simplified session fixture from pyramid_fullauth tests:

from sqlalchemy import create_engine
from sqlalchemy.orm import scoped_session, sessionmaker
from sqlalchemy.pool import NullPool
from zope.sqlalchemy import register


@pytest.fixture
def db_session(postgresql):
    """Session for SQLAlchemy."""
    from pyramid_fullauth.models import Base

    connection = f'postgresql+psycopg2://{postgresql.info.user}:@{postgresql.info.host}:{postgresql.info.port}/{postgresql.info.dbname}'

    engine = create_engine(connection, echo=False, poolclass=NullPool)
    pyramid_basemodel.Session = scoped_session(sessionmaker(extension=ZopeTransactionExtension()))
    pyramid_basemodel.bind_engine(
        engine, pyramid_basemodel.Session, should_create=True, should_drop=True)

    yield pyramid_basemodel.Session

    transaction.commit()
    Base.metadata.drop_all(engine)


@pytest.fixture
def user(db_session):
    """Test user fixture."""
    from pyramid_fullauth.models import User
    from tests.tools import DEFAULT_USER

    new_user = User(**DEFAULT_USER)
    db_session.add(new_user)
    transaction.commit()
    return new_user


def test_remove_last_admin(db_session, user):
    """
    Sample test checks internal login, but shows usage in tests with SQLAlchemy
    """
    user = db_session.merge(user)
    user.is_admin = True
    transaction.commit()
    user = db_session.merge(user)

    with pytest.raises(AttributeError):
        user.is_admin = False

Note

See the original code at pyramid_fullauth's conftest file. Depending on your needs, that in between code can fire alembic migrations in case of sqlalchemy stack or any other code

Maintaining database state outside of the fixtures

It is possible and appears it's used in other libraries for tests, to maintain database state with the use of the pytest-postgresql database managing functionality:

For this import DatabaseJanitor and use its init and drop methods:

import pytest
from pytest_postgresql.janitor import DatabaseJanitor

@pytest.fixture
def database(postgresql_proc):
    # variable definition

    janitor = DatabaseJanitor(
        postgresql_proc.user,
        postgresql_proc.host,
        postgresql_proc.port,
        "my_test_database",
        postgresql_proc.version,
        password="secret_password,
    ):
    janitor.init()
    yield psycopg2.connect(
        dbname="my_test_database",
        user=postgresql_proc.user,
        password="secret_password",
        host=postgresql_proc.host,
        port=postgresql_proc.port,
    )
    janitor.drop()

or use it as a context manager:

import pytest
from pytest_postgresql.janitor import DatabaseJanitor

@pytest.fixture
def database(postgresql_proc):
    # variable definition

    with DatabaseJanitor(
        postgresql_proc.user,
        postgresql_proc.host,
        postgresql_proc.port,
        "my_test_database",
        postgresql_proc.version,
        password="secret_password,
    ):
        yield psycopg2.connect(
            dbname="my_test_database",
            user=postgresql_proc.user,
            password="secret_password",
            host=postgresql_proc.host,
            port=postgresql_proc.port,
        )

Note

DatabaseJanitor manages the state of the database, but you'll have to create connection to use in test code yourself.

You can optionally pass in a recognized postgresql ISOLATION_LEVEL for additional control.

Note

See DatabaseJanitor usage in python's warehouse test code https://github.com/pypa/warehouse/blob/5d15bfe/tests/conftest.py#L127

Connecting to Postgresql (in a docker)

To connect to a docker run postgresql and run test on it, use noproc fixtures.

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

This will start postgresql in a docker container, however using a postgresql installed locally is not much different.

In tests, make sure that all your tests are using postgresql_noproc fixture like that:

postgresql_in_docker = factories.postgresql_noproc()
postresql = factories.postgresql("postgresql_in_docker", db_name="test")


def test_postgres_docker(postresql):
    """Run test."""
    cur = postgresql.cursor()
    cur.execute("CREATE TABLE test (id serial PRIMARY KEY, num integer, data varchar);")
    postgresql.commit()
    cur.close()

And run tests:

pytest --postgresql-host=172.17.0.2 --postgresql-password=mysecretpassword

Using a common database initialisation between tests

If you've got several tests that require common initialisation, you need to define a load and pass it to your custom postgresql process fixture:

import pytest_postgresql.factories
def load_database(**kwargs):
    db_connection: connection = psycopg2.connect(**kwargs)
    with db_connection.cursor() as cur:
        cur.execute("CREATE TABLE stories (id serial PRIMARY KEY, name varchar);")
        cur.execute(
            "INSERT INTO stories (name) VALUES"
            "('Silmarillion'), ('Star Wars'), ('The Expanse'), ('Battlestar Galactica')"
        )
        db_connection.commit()

postgresql_proc = factories.postgresql_proc(
    load=[load_database],
)

postgresql = factories.postgresql(
    "postgresql_proc",
)

You can also define your own database name by passing same dbname value to both factories.

The way this will work is that the process fixture will populate template database, which in turn will be used automatically by client fixture to create a test database from scratch. Fast, clean and no dangling transactions, that could be accidentally rolled back.

Same approach will work with noproces fixture, while connecting to already running postgresql instance whether it'll be on a docker machine or running remotely or locally.

Owner
Clearcode
Software house with a passion for technology. We specialize in building enterprise-grade adtech, martech and analytics platforms.
Clearcode
A Python program that will log into your scheduled Google Meets hands free

Chrome GMautomation General Information This Python program will open up Chrome and log into your scheduled Google Meet with camera and mic turned off

Jonathan Leow 5 Dec 31, 2021
Voip Open Linear Testing Suite

VOLTS Voip Open Linear Tester Suite Functional tests for VoIP systems based on voip_patrol and docker 10'000 ft. view System is designed to run simple

Igor Olhovskiy 17 Dec 30, 2022
Free cleverbot without headless browser

Cleverbot Scraper Simple free cleverbot library that doesn't require running a heavy ram wasting headless web browser to actually chat with the bot, a

Matheus Fillipe 3 Sep 25, 2022
Language-agnostic HTTP API Testing Tool

Dredd β€” HTTP API Testing Framework Dredd is a language-agnostic command-line tool for validating API description document against backend implementati

Apiary 4k Jan 05, 2023
hCaptcha solver and bypasser for Python Selenium. Simple website to try to solve hCaptcha.

hCaptcha solver for Python Selenium. Many thanks to engageub for his hCaptcha solver userscript. This script is solely intended for the use of educati

Maxime DrΓ©an 59 Dec 25, 2022
A tool to auto generate the basic mocks and asserts for faster unit testing

Mock Generator A tool to generate the basic mocks and asserts for faster unit testing. πŸŽ‰ New: you can now use pytest-mock-generator, for more fluid p

31 Dec 24, 2022
Travel through time in your tests.

time-machine Travel through time in your tests. A quick example: import datetime as dt

Adam Johnson 373 Dec 27, 2022
βœ… Python web automation and testing. πŸš€ Fast, easy, reliable. πŸ’ 

Build fast, reliable, end-to-end tests. SeleniumBase is a Python framework for web automation, end-to-end testing, and more. Tests are run with "pytes

SeleniumBase 3k Jan 04, 2023
The successor to nose, based on unittest2

Welcome to nose2 nose2 is the successor to nose. It's unittest with plugins. nose2 is a new project and does not support all of the features of nose.

736 Dec 16, 2022
Hypothesis is a powerful, flexible, and easy to use library for property-based testing.

Hypothesis Hypothesis is a family of testing libraries which let you write tests parametrized by a source of examples. A Hypothesis implementation the

Hypothesis 6.4k Jan 05, 2023
GitHub action for AppSweep Mobile Application Security Testing

GitHub action for AppSweep can be used to continuously integrate app scanning using AppSweep into your Android app build process

Guardsquare 14 Oct 06, 2022
A rewrite of Python's builtin doctest module (with pytest plugin integration) but without all the weirdness

The xdoctest package is a re-write of Python's builtin doctest module. It replaces the old regex-based parser with a new abstract-syntax-tree based pa

Jon Crall 174 Dec 16, 2022
Fidelipy - Semi-automated trading on fidelity.com

fidelipy fidelipy is a simple Python 3.7+ library for semi-automated trading on fidelity.com. The scope is limited to the Trade Stocks/ETFs simplified

Darik Harter 8 May 10, 2022
Selenium-python but lighter: Helium is the best Python library for web automation.

Selenium-python but lighter: Helium Selenium-python is great for web automation. Helium makes it easier to use. For example: Under the hood, Helium fo

Michael Herrmann 3.2k Dec 31, 2022
Python scripts for a generic performance testing infrastructure using Locust.

TODOs Reference to published paper or online version of it loadtest_plotter.py: Cleanup and reading data from files ARS_simulation.py: Cleanup, docume

Juri Tomak 3 Dec 15, 2022
WomboAI Art Generator

WomboAI Art Generator Automate AI art generation using wombot.art. Also integrated into SnailBot for you to try out. Setup Install Python Go to the py

nbee 7 Dec 03, 2022
A testing system for catching visual regressions in Web applications.

Huxley Watches you browse, takes screenshots, tells you when they change Huxley is a test-like system for catching visual regressions in Web applicati

Facebook Archive 4.1k Nov 30, 2022
No longer maintained, please migrate to model_bakery

Model Mommy: Smart fixtures for better tests IMPORTANT: Model Mommy is no longer maintained and was replaced by Model Bakery. Please, consider migrati

Bernardo Fontes 917 Oct 04, 2022
Doing dirty (but extremely useful) things with equals.

Doing dirty (but extremely useful) things with equals. Documentation: dirty-equals.helpmanual.io Source Code: github.com/samuelcolvin/dirty-equals dir

Samuel Colvin 602 Jan 05, 2023