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Airflow Test Infrastructure

  • Unit tests are Python tests that do not require any additional integrations. Unit tests are available both in the Breeze environment and local virtualenv.
  • Integration tests are available in the Breeze development environment that is also used for Airflow Travis CI tests. Integration test are special tests that require additional services running - such as Postgres/Mysql/Kerberos etc. Those tests are not yet clearly marked as integration tests but soon they will be clearly separated by pytest annotations.
  • System tests are automatic tests that use external systems like Google Cloud Platform. These tests are intended for an end-to-end DAG execution. Note that automated execution of these tests is still work in progress.

This document is about running python tests, before the tests are run we also use static code checks which allow to catch typical errors in code before tests are executed.

Airflow Unit Tests

All tests for Apache Airflow are run using pytest .

Writing unit tests

There are a few guidelines that you should follow when writing unit tests:

  • Standard unit tests that do not require integrations with external systems should mock all communication
  • All our tests are run with pytest make sure you set your IDE/runners (see below) to use pytest by default
  • For new tests we should use standard "asserts" of python and pytest decorators/context managers for testing rather than unittest ones. Look at Pytest docs for details.
  • We use parameterized framework for tests that have variations in parameters
  • We plann to convert all unittests to standard "asserts" semi-automatically but this will be done later in Airflow 2.0 development phase. That will include setUp/tearDown/context managers and decorators

Running Unit Tests from IDE

To run unit tests from the IDE, create the local virtualenv, select it as the default project's environment, then configure your test runner:

Configuring test runner

and run unit tests as follows:

Running unit tests

Note that you can run the unit tests in the standalone local virtualenv (with no Breeze installed) if they do not have dependencies such as Postgres/MySQL/Hadoop/etc.

Running Unit Tests

To run unit, integration and system tests from the Breeze and your virtualenv you can use pytest framework.

Custom pytest plugin run airflow db init and airflow db reset the first time you launch them, so you can count on the database being initialized. Currently, when you run tests not supported in the local virtualenv, the tests may either fail or provide an error message.

There are many available options for selecting specific test in pytest. Details could be found in official documentation but here are few basic examples:

pytest -k "TestCore and not check"

This will run TestCore class but will skip tests of this class that includes 'check' in their names. For better performance (due to test collection) you should do:

pytest tests/tests_core.py -k "TestCore and not bash".

This flag is useful when used like this:

pytest tests/tests_core.py -k "test_check_operators"

to run single test. This can also be done by specifying full path to the test:

pytest tests/test_core.py::TestCore::test_check_operators

To run whole test class:

pytest tests/test_core.py::TestCore

You can use all available pytest flags, for example to increase log level for debugging purposes:

pytest --log-level=DEBUG tests/test_core.py::TestCore

Note: We do not provide a clear distinction between tests (Unit/Integration/System tests), but we are working on it.

Running Tests for a Specified Target using Breeze from the host

If you wish to only run tests and not to drop into shell, you can do this by providing the -t, --test-target flag. You can add extra pytest flags after -- in the command line.

./breeze --test-target tests/hooks/test_druid_hook.py -- --logging-level=DEBUG

You can run the whole test suite with a special '.' test target:

./breeze --test-target .

You can also specify individual tests or a group of tests:

./breeze --test-target tests/test_core.py::TestCore

Running Full test suite via scripts from the host

To run all tests with default settings (Python 3.6, Sqlite backend, "docker" environment), enter:

./scripts/ci/local_ci_run_airflow_testing.sh

To select Python 3.6 version, Postgres backend, and a docker environment, specify:

PYTHON_VERSION=3.6 BACKEND=postgres ENV=docker ./scripts/ci/local_ci_run_airflow_testing.sh

To run Kubernetes tests, enter:

KUBERNETES_VERSION==v1.13.5 KUBERNETES_MODE=persistent_mode BACKEND=postgres ENV=kubernetes \
  ./scripts/ci/local_ci_run_airflow_testing.sh
  • PYTHON_VERSION is one of 3.6/3.7
  • BACKEND is one of postgres/sqlite/mysql
  • ENV is one of docker/kubernetes/bare
  • KUBERNETES_VERSION is required for Kubernetes tests. Currently, it is KUBERNETES_VERSION=v1.13.0.
  • KUBERNETES_MODE is a mode of kubernetes: either persistent_mode or git_mode.

Airflow Integration Tests

Running Airflow integration tests cannot be run in local virtualenv. They can only run in Breeze environment locally and in Travis CI.

When you are in Breeze environment you can execute both Unit and Integration tests.

Travis CI Testing Framework

Airflow test suite is based on Travis CI framework as running all of the tests locally requires significant setup. You can set up Travis CI in your fork of Airflow by following the Travis CI Getting Started guide.

Consider using Travis CI framework if you submit multiple pull requests and want to speed up your builds.

There are two different options available for running Travis CI, and they are set up on GitHub as separate components:

  • Travis CI GitHub App (new version)
  • Travis CI GitHub Services (legacy version)

Travis CI GitHub App (new version)

  1. Once installed, configure the Travis CI GitHub App at Configure Travis CI.
  2. Set repository access to either "All repositories" for convenience, or "Only select repositories" and choose USERNAME/airflow in the drop-down menu.
  3. Access Travis CI for your fork at https://travis-ci.com/USERNAME/airflow.

Travis CI GitHub Services (legacy version)

NOTE: The apache/airflow project is still using the legacy version.

Travis CI GitHub Services version uses an Authorized OAuth App.

  1. Once installed, configure the Travis CI Authorized OAuth App at Travis CI OAuth APP.
  2. If you are a GitHub admin, click the Grant button next to your organization; otherwise, click the Request button. For the Travis CI Authorized OAuth App, you may have to grant access to the forked ORGANIZATION/airflow repo even though it is public.
  3. Access Travis CI for your fork at https://travis-ci.org/ORGANIZATION/airflow.

Creating New Projects in Travis CI

If you need to create a new project in Travis CI, use travis-ci.com for both private repos and open source.

The travis-ci.org site for open source projects is now legacy and you should not use it.

More information:

Airflow System Tests

The System tests for Airflow are not yet fully implemented. They are Work In Progress of the AIP-4 Support for System Tests for external systems. These tests need to communicate with external services/systems that are available if you have appropriate credentials configured for your tests. The tests derive from tests.system_test_class.SystemTests class.

The system tests execute a specified example DAG file that runs the DAG end-to-end.

An example of such a system test is airflow.tests.providers.google.operators.test_natural_language_system.CloudNaturalLanguageExampleDagsTest.

For now you can execute the system tests and follow messages printed to get them running. Soon more information on running the tests will be available.

Local and Remote Debugging

One of the great benefits of using the local virtualenv and Breeze is an option to run local debugging in your IDE graphical interface. You can also use ipdb if you prefer console debugging.

When you run example DAGs, even if you run them using unit tests within IDE, they are run in a separate container. This makes it a little harder to use with IDE built-in debuggers. Fortunately, IntelliJ/PyCharm provides an effective remote debugging feature (but only in paid versions). See additional details on remote debugging.

You can set up your remote debugging session as follows:

Setup remote debugging

Note that on macOS, you have to use a real IP address of your host rather than default localhost because on macOS the container runs in a virtual machine with a different IP address.

Make sure to configure source code mapping in the remote debugging configuration to map your local sources to the /opt/airflow location of the sources within the container:

Source code mapping

DAG testing

To ease and speed up process of developing DAGs you can use :class:`~airflow.executors.debug_executor.DebugExecutor` - a single process executor for debugging purposes. Using this executor you can run and debug DAGs from your IDE.

IDE setup steps:

1. Add main block at the end of your DAG file to make it runnable. It will run a backfill job:

if __name__ == '__main__':
  dag.clear(reset_dag_runs=True)
  dag.run()
  1. Setup AIRFLOW__CORE__EXECUTOR=DebugExecutor in run configuration of your IDE. In this step you should also setup all environment variables required by your DAG.
  2. Run / debug the DAG file.

Additionally DebugExecutor can be used in a fail-fast mode that will make all other running or scheduled tasks fail immediately. To enable this option set AIRFLOW__DEBUG__FAIL_FAST=True or adjust fail_fast option in your airflow.cfg.