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
pytest plugin for a better developer experience when working with the PyTorch test suite

pytest-pytorch What is it? pytest-pytorch is a lightweight pytest-plugin that enhances the developer experience when working with the PyTorch test sui

Quansight 39 Nov 18, 2022
Load and performance benchmark tool

Yandex Tank Yandextank has been moved to Python 3. Latest stable release for Python 2 here. Yandex.Tank is an extensible open source load testing tool

Yandex 2.2k Jan 03, 2023
Implement unittest, removing all global variable and returning values

Implement unittest, removing all global variable and returning values

Placide 1 Nov 01, 2021
pytest splinter and selenium integration for anyone interested in browser interaction in tests

Splinter plugin for the pytest runner Install pytest-splinter pip install pytest-splinter Features The plugin provides a set of fixtures to use splin

pytest-dev 238 Nov 14, 2022
Fail tests that take too long to run

GitHub | PyPI | Issues pytest-fail-slow is a pytest plugin for making tests fail that take too long to run. It adds a --fail-slow DURATION command-lin

John T. Wodder II 4 Nov 27, 2022
Generate random test credit card numbers for testing, validation and/or verification purposes.

Generate random test credit card numbers for testing, validation and/or verification purposes.

Dark Hunter 141 5 Nov 14, 2022
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
Redis fixtures and fixture factories for Pytest.

Redis fixtures and fixture factories for Pytest.This is a pytest plugin, that enables you to test your code that relies on a running Redis database. It allows you to specify additional fixtures for R

Clearcode 86 Dec 23, 2022
Useful additions to Django's default TestCase

django-test-plus Useful additions to Django's default TestCase from REVSYS Rationale Let's face it, writing tests isn't always fun. Part of the reason

REVSYS 546 Dec 22, 2022
Show coverage stats online via coveralls.io

Coveralls for Python Test Status: Version Info: Compatibility: Misc: coveralls.io is a service for publishing your coverage stats online. This package

Kevin James 499 Dec 28, 2022
Python dilinin Selenium kütüphanesini kullanarak; Amazon, LinkedIn ve ÇiçekSepeti üzerinde test işlemleri yaptığımız bir case study reposudur.

Python dilinin Selenium kütüphanesini kullanarak; Amazon, LinkedIn ve ÇiçekSepeti üzerinde test işlemleri yaptığımız bir case study reposudur. LinkedI

Furkan Gulsen 8 Nov 01, 2022
Python version of the Playwright testing and automation library.

🎭 Playwright for Python Docs | API Playwright is a Python library to automate Chromium, Firefox and WebKit browsers with a single API. Playwright del

Microsoft 7.8k Jan 02, 2023
A set of pytest fixtures to test Flask applications

pytest-flask An extension of pytest test runner which provides a set of useful tools to simplify testing and development of the Flask extensions and a

pytest-dev 433 Dec 23, 2022
This is a web test framework based on python+selenium

Basic thoughts for this framework There should have a BasePage.py to be the parent page and all the page object should inherit this class BasePage.py

Cactus 2 Mar 09, 2022
Main purpose of this project is to provide the service to automate the API testing process

PPTester project Main purpose of this project is to provide the service to automate the API testing process. In order to deploy this service use you s

4 Dec 16, 2021
Code coverage measurement for Python

Coverage.py Code coverage testing for Python. Coverage.py measures code coverage, typically during test execution. It uses the code analysis tools and

Ned Batchelder 2.3k Jan 04, 2023
A simple tool to test internet stability.

pingtest Description A personal project for testing internet stability, intended for use in Linux and Windows.

chris 0 Oct 17, 2021
This file will contain a series of Python functions that use the Selenium library to search for elements in a web page while logging everything into a file

element_search with Selenium (Now With docstrings 😎 ) Just to mention, I'm a beginner to all this, so it it's very possible to make some mistakes The

2 Aug 12, 2021
Descriptor Vector Exchange

Descriptor Vector Exchange This repo provides code for learning dense landmarks without supervision. Our approach is described in the ICCV 2019 paper

James Thewlis 74 Nov 29, 2022
0hh1 solver for the web (selenium) and also for mobile (adb)

0hh1 - Solver Aims to solve the '0hh1 puzzle' for all the sizes (4x4, 6x6, 8x8, 10x10 12x12). for both the web version (using selenium) and on android

Adwaith Rajesh 1 Nov 05, 2021