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Airflow Unit Tests

All unit tests for Apache Airflow are run using pytest .

Writing Unit Tests

Follow the guidelines when writing unit tests:

  • For standard unit tests that do not require integrations with external systems, make sure to simulate all communications.
  • All Airflow tests are run with pytest. Make sure to set your IDE/runners (see below) to use pytest by default.
  • For new tests, use standard "asserts" of Python and pytest decorators/context managers for testing rather than unittest ones. See pytest docs for details.
  • Use a parameterized framework for tests that have variations in parameters.
  • Use with pytest.warn to capture warnings rather than recwarn fixture. We are aiming for 0-warning in our tests, so we run Pytest with --disable-warnings but instead we have pytest-capture-warnings plugin that overrides recwarn fixture behaviour.

Airflow configuration for unit tests

Some of the unit tests require special configuration set as the default. This is done automatically by adding AIRFLOW__CORE__UNIT_TEST_MODE=True to the environment variables in Pytest auto-used fixture. This in turn makes Airflow load test configuration from the file airflow/config_templates/unit_tests.cfg. Test configuration from there replaces the original defaults from airflow/config_templates/config.yml. If you want to add some test-only configuration, as default for all tests you should add the value to this file.

You can also - of course - override the values in individual test by patching environment variables following the usual AIRFLOW__SECTION__KEY pattern or conf_vars context manager.

Airflow unit test types

Airflow tests in the CI environment are split into several test types. You can narrow down which test types you want to use in various breeze testing sub-commands in three ways:

  • via specifying the --test-type when you run single test type in breeze testing tests command
  • via specifying space separating list of test types via --paralleltest-types or --exclude-parallel-test-types options when you run tests in parallel (in several testing commands)

Those test types are defined:

  • Always - those are tests that should be always executed (always sub-folder)
  • API - Tests for the Airflow API (api, api_connexion, api_experimental and api_internal sub-folders)
  • CLI - Tests for the Airflow CLI (cli folder)
  • Core - for the core Airflow functionality (core, executors, jobs, models, ti_deps, utils sub-folders)
  • Operators - tests for the operators (operators folder with exception of Virtualenv Operator tests and External Python Operator tests that have their own test type). They are skipped by the

virtualenv_operator and external_python_operator test markers that the tests are marked with. * WWW - Tests for the Airflow webserver (www folder) * Providers - Tests for all Providers of Airflow (providers folder) * PlainAsserts - tests that require disabling assert-rewrite feature of Pytest (usually because

a buggy/complex implementation of an imported library) (plain_asserts marker)
  • Other - all other tests remaining after the above tests are selected

There are also Virtualenv/ExternalPython operator test types that are excluded from Operators test type and run as separate test types. Those are :

  • PythonVenv - tests for PythonVirtualenvOperator - selected directly as TestPythonVirtualenvOperator
  • BranchPythonVenv - tests for BranchPythonVirtualenvOperator - selected directly as TestBranchPythonVirtualenvOperator
  • ExternalPython - tests for ExternalPythonOperator - selected directly as TestExternalPythonOperator
  • BranchExternalPython - tests for BranchExternalPythonOperator - selected directly as TestBranchExternalPythonOperator

We have also tests that run "all" tests (so they do not look at the folder, but at the pytest markers the tests are marked with to run with some filters applied.

  • All-Postgres - tests that require Postgres database. They are only run when backend is Postgres (backend("postgres") marker)
  • All-MySQL - tests that require MySQL database. They are only run when backend is MySQL (backend("mysql") marker)
  • All-Quarantined - tests that are flaky and need to be fixed (quarantined marker)
  • All - all tests are run (this is the default)

We also have Integration tests that are running Integration tests with external software that is run via --integration flag in breeze environment - via breeze testing integration-tests.

  • Integration - tests that require external integration images running in docker-compose

This is done for three reasons:

  1. in order to selectively run only subset of the test types for some PRs
  2. in order to allow efficient parallel test execution of the tests on Self-Hosted runners

For case 2. We can utilize memory and CPUs available on both CI and local development machines to run test in parallel, but we cannot use pytest xdist plugin for that - we need to split the tests into test types and run each test type with their own instance of database and separate container where the tests in each type are run with exclusive access to their database and each test within test type runs sequentially. By the nature of those tests - they rely on shared databases - and they update/reset/cleanup data in the databases while they are executing.

DB and non-DB tests

There are two kinds of unit tests in Airflow - DB and non-DB tests. This chapter describe the differences between those two types.

Airflow non-DB tests

For the Non-DB tests, they are run once for each tested Python version with none database backend (which causes any database access to fail. Those tests are run with pytest-xdist plugin in parallel which means that we can efficiently utilised multi-processor machines (including self-hosted runners with 8 CPUS we have to run the tests with maximum parallelism).

It's usually straightforward to run those tests in local virtualenv because they do not require any setup or running database. They also run much faster than DB tests. You can run them with pytest command or with breeze that has all the dependencies needed to run all tests automatically installed. Of course you can also select just specific test or folder or module for the Pytest to collect/run tests from there, the example below shows how to run all tests, parallelizing them with pytest-xdist (by specifying tests folder):

pytest tests --skip-db-tests -n auto

The --skip-db-tests flag will only run tests that are not marked as DB tests.

You can also run breeze command to run all the tests (they will run in a separate container, the selected python version and without access to any database). Adding --use-xdist flag will run all tests in parallel using pytest-xdist plugin.

We have a dedicated, opinionated breeze testing non-db-tests command as well that runs non-DB tests (it is also used in CI to run the non-DB tests, where you do not have to specify extra flags for parallel running and you can run all the Non-DB tests (or just a subset of them with --parallel-test-types or --exclude-parallel-test-types) in parallel:

breeze testing non-db-tests

You can pass --parallel-test-type list of test types to execute or --exclude--parallel-test-types to exclude them from the default set:.

breeze testing non-db-tests --parallel-test-types "Providers API CLI"
breeze testing non-db-tests --exclude-parallel-test-types "Providers API CLI"

You can also run the same commands via breeze testing tests - by adding the necessary flags manually:

breeze testing tests --skip-db-tests --backend none --use-xdist

Also you can enter interactive shell with breeze and run tests from there if you want to iterate with the tests. Source files in breeze are mounted as volumes so you can modify them locally and rerun in Breeze as you will (-n auto will parallelize tests using pytest-xdist plugin):

breeze shell --backend none --python 3.8
> pytest tests --skip-db-tests -n auto

Airflow DB tests

Some of the tests of Airflow require a database to connect to in order to run. Those tests store and read data from Airflow DB using Airflow's core code and it's crucial to run the tests against all real databases that Airflow supports in order to check if the SQLAlchemy queries are correct and if the database

schema is correct.

Those tests should be marked with @pytest.mark.db decorator on one of the levels:

  • test method can be marked with @pytest.mark.db decorator
  • test class can be marked with @pytest.mark.db decorator
  • test module can be marked with pytestmark = pytest.mark.db at the top level of the module

For the DB tests, they are run against the multiple databases Airflow support, multiple versions of those and multiple Python versions it supports. In order to save time for testing not all combinations are tested but enough various combinations are tested to detect potential problems.

By default, the DB tests will use sqlite and the "airflow.db" database created and populated in the ${AIRFLOW_HOME} folder. You do not need to do anything to get the database created and initialized, but if you need to clean and restart the db, you can run tests with -with-db-init flag - then the database will be re-initialized. You can also set AIRFLOW__DATABASE__SQL_ALCHEMY_CONN environment variable to point to supported database (Postgres, MySQL, etc.) and the tests will use that database. You might need to run airflow db reset to initialize the database in that case.

The "non-DB" tests are perfectly fine to run when you have database around but if you want to just run DB tests (as happens in our CI for the Database runs) you can use --run-db-tests-only flag to filter out non-DB tests (and obviously you can specify not only on the whole tests directory but on any folders/files/tests selection, pytest supports).

pytest tests/ --run-db-tests-only

You can also run DB tests with breeze dockerized environment. You can choose backend to use with --backend flag. The default is sqlite but you can also use others such as postgres or mysql. You can also select backend version and Python version to use. You can specify the test-type to run - breeze will list the test types you can run with --help and provide auto-complete for them. Example below runs the Core tests with postgres backend and 3.8 Python version:

We have a dedicated, opinionated breeze testing db-tests command as well that runs DB tests (it is also used in CI to run the DB tests, where you do not have to specify extra flags for parallel running and you can run all the DB tests (or just a subset of them with --parallel-test-types or --exclude-parallel-test-types) in parallel:

breeze testing non-db-tests --backend postgres

You can pass --parallel-test-type list of test types to execute or --exclude--parallel-test-types to exclude them from the default set:.

breeze testing db-tests --parallel-test-types "Providers API CLI"
breeze testing db-tests --exclude-parallel-test-types "Providers API CLI"

You can also run the same commands via breeze testing tests - by adding the necessary flags manually:

breeze testing tests --run-db-tests-only --backend postgres --run-tests-in-parallel

Also - if you want to iterate with the tests you can enter interactive shell and run the tests iteratively - either by package/module/test or by test type - whatever pytest supports.

breeze shell --backend postgres --python 3.8
> pytest tests --run-db-tests-only

As explained before, you cannot run DB tests in parallel using pytest-xdist plugin, but breeze has support to split all the tests into test-types to run in separate containers and with separate databases and you can run the tests using --run-tests-in-parallel flag (which is automatically enabled when you use breeze testing db-tests command):

breeze testing tests --run-db-tests-only --backend postgres --python 3.8 --run-tests-in-parallel

Examples of marking test as DB test

You can apply the marker on method/function/class level with @pytest.mark.db_test decorator or at the module level with pytestmark = pytest.mark.db_test at the top level of the module.

It's up to the author to decide whether to mark the test, class, or module as "DB-test" - generally the less DB tests - the better and if we can clearly separate the parts that are DB from non-DB, we should, but also it's ok if few tests are marked as DB tests when they are not but they are part of the class or module that is "mostly-DB".

Sometimes, when your class can be clearly split to DB and non-DB parts, it's better to split the class into two separate classes and mark only the DB class as DB test.

Method level:

import pytest


@pytest.mark.db_test
def test_add_tagging(self, sentry, task_instance):
    ...

Class level:

import pytest


@pytest.mark.db_test
class TestDatabricksHookAsyncAadTokenSpOutside:
    ...

Module level (at the top of the module):

import pytest

from airflow.models.baseoperator import BaseOperator
from airflow.models.dag import DAG
from airflow.ti_deps.dep_context import DepContext
from airflow.ti_deps.deps.task_concurrency_dep import TaskConcurrencyDep

pytestmark = pytest.mark.db_test

Best practices for DB tests

Usually when you add new tests you add tests "similar" to the ones that are already there. In most cases, therefore you do not have to worry about the test type - it will be automatically selected for you by the fact that the Test Class that you add the tests or the whole module will be marked with db_test marker.

You should strive to write "pure" non-db unit tests (i.e. DB tests) but sometimes it's just better to plug-in the existing framework of DagRuns, Dags, Connections and Variables to use the Database directly rather than having to mock the DB access for example. It's up to you to decide.

However, if you choose to write DB tests you have to make sure you add the db_test marker - either to the test method, class (with decorator) or whole module (with pytestmark at the top level of the module).

In most cases when you add tests to existing modules or classes, you follow similar tests so you do not have to do anything, but in some cases you need to decide if your test should be marked as DB test or whether it should be changed to not use the database at all.

If your test accesses the database but is not marked properly the Non-DB test in CI will fail with this message:

"Your test accessed the DB but `_AIRFLOW_SKIP_DB_TESTS` is set.
Either make sure your test does not use database or mark your test with `@pytest.mark.db_test`.

How to verify if DB test is correctly classified

When you add if you want to see if your DB test is correctly classified, you can run the test or group of tests with --skip-db-tests flag.

You can run the all (or subset of) test types if you want to make sure all ot the problems are fixed

breeze testing tests --skip-db-tests tests/your_test.py

For the whole test suite you can run:

breeze testing non-db-tests

For selected test types (example - the tests will run for Providers/API/CLI code only:

breeze testing non-db-tests --parallel-test-types "Providers API CLI"

How to make your test not depend on DB

This is tricky and there is no single solution. Sometimes we can mock-out the methods that require DB access or objects that normally require database. Sometimes we can decide to test just sinle method of class rather than more complex set of steps. Generally speaking it's good to have as many "pure" unit tests that require no DB as possible comparing to DB tests. They are usually faster an more reliable as well.

Special cases

There are some tricky test cases that require special handling. Here are some of them:

Parameterized tests stability

The parameterized tests require stable order of parameters if they are run via xdist - because the parameterized tests are distributed among multiple processes and handled separately. In some cases the parameterized tests have undefined / random order (or parameters are not hashable - for example set of enums). In such cases the xdist execution of the tests will fail and you will get an error mentioning "Known Limitations of xdist". You can see details about the limitation here

The error in this case will look similar to:

Different tests were collected between gw0 and gw7. The difference is:

The fix for that is to sort the parameters in parametrize. For example instead of this:

@pytest.mark.parametrize("status", ALL_STATES)
def test_method():
    ...

do that:

@pytest.mark.parametrize("status", sorted(ALL_STATES))
def test_method():
    ...

Similarly if your parameters are defined as result of utcnow() or other dynamic method - you should avoid that, or assign unique IDs for those parametrized tests. Instead of this:

@pytest.mark.parametrize(
    "url, expected_dag_run_ids",
    [
        (
            f"api/v1/dags/TEST_DAG_ID/dagRuns?end_date_gte="
            f"{urllib.parse.quote((timezone.utcnow() + timedelta(days=1)).isoformat())}",
            [],
        ),
        (
            f"api/v1/dags/TEST_DAG_ID/dagRuns?end_date_lte="
            f"{urllib.parse.quote((timezone.utcnow() + timedelta(days=1)).isoformat())}",
            ["TEST_DAG_RUN_ID_1", "TEST_DAG_RUN_ID_2"],
        ),
    ],
)
def test_end_date_gte_lte(url, expected_dag_run_ids):
    ...

Do this:

@pytest.mark.parametrize(
    "url, expected_dag_run_ids",
    [
        pytest.param(
            f"api/v1/dags/TEST_DAG_ID/dagRuns?end_date_gte="
            f"{urllib.parse.quote((timezone.utcnow() + timedelta(days=1)).isoformat())}",
            [],
            id="end_date_gte",
        ),
        pytest.param(
            f"api/v1/dags/TEST_DAG_ID/dagRuns?end_date_lte="
            f"{urllib.parse.quote((timezone.utcnow() + timedelta(days=1)).isoformat())}",
            ["TEST_DAG_RUN_ID_1", "TEST_DAG_RUN_ID_2"],
            id="end_date_lte",
        ),
    ],
)
def test_end_date_gte_lte(url, expected_dag_run_ids):
    ...
Problems with Non-DB test collection

Sometimes, even if whole module is marked as @pytest.mark.db_test even parsing the file and collecting tests will fail when --skip-db-tests is used because some of the imports od objects created in the module will read the database.

Usually what helps is to move such initialization code to inside the tests or pytest fixtures (and pass objects needed by tests as fixtures rather than importing them from the module). Similarly you might use DB - bound objects (like Connection) in your parametrize specification - this will also fail pytest collection. Move creation of such objects to inside the tests:

Moving object creation from top-level to inside tests. This code will break collection of tests even if the test is marked as DB test:

TI = TaskInstance(
    task=BashOperator(task_id="test", bash_command="true", dag=DAG(dag_id="id"), start_date=datetime.now()),
    run_id="fake_run",
    state=State.RUNNING,
)


class TestCallbackRequest:
    @pytest.mark.parametrize(
        "input,request_class",
        [
            (CallbackRequest(full_filepath="filepath", msg="task_failure"), CallbackRequest),
            (
                TaskCallbackRequest(
                    full_filepath="filepath",
                    simple_task_instance=SimpleTaskInstance.from_ti(ti=TI),
                    processor_subdir="/test_dir",
                    is_failure_callback=True,
                ),
                TaskCallbackRequest,
            ),
            (
                DagCallbackRequest(
                    full_filepath="filepath",
                    dag_id="fake_dag",
                    run_id="fake_run",
                    processor_subdir="/test_dir",
                    is_failure_callback=False,
                ),
                DagCallbackRequest,
            ),
            (
                SlaCallbackRequest(
                    full_filepath="filepath",
                    dag_id="fake_dag",
                    processor_subdir="/test_dir",
                ),
                SlaCallbackRequest,
            ),
        ],
    )
    def test_from_json(self, input, request_class):
        ...

Instead - this will not break collection. The TaskInstance is not initialized when the module is parsed, it will only be initialized when the test gets executed because we moved initialization of it from top level / parametrize to inside the test:

pytestmark = pytest.mark.db_test


class TestCallbackRequest:
    @pytest.mark.parametrize(
        "input,request_class",
        [
            (CallbackRequest(full_filepath="filepath", msg="task_failure"), CallbackRequest),
            (
                None,  # to be generated when test is run
                TaskCallbackRequest,
            ),
            (
                DagCallbackRequest(
                    full_filepath="filepath",
                    dag_id="fake_dag",
                    run_id="fake_run",
                    processor_subdir="/test_dir",
                    is_failure_callback=False,
                ),
                DagCallbackRequest,
            ),
            (
                SlaCallbackRequest(
                    full_filepath="filepath",
                    dag_id="fake_dag",
                    processor_subdir="/test_dir",
                ),
                SlaCallbackRequest,
            ),
        ],
    )
    def test_from_json(self, input, request_class):
        if input is None:
            ti = TaskInstance(
                task=BashOperator(
                    task_id="test", bash_command="true", dag=DAG(dag_id="id"), start_date=datetime.now()
                ),
                run_id="fake_run",
                state=State.RUNNING,
            )

            input = TaskCallbackRequest(
                full_filepath="filepath",
                simple_task_instance=SimpleTaskInstance.from_ti(ti=ti),
                processor_subdir="/test_dir",
                is_failure_callback=True,
            )

Sometimes it is difficult to rewrite the tests, so you might add conditional handling and mock out some database-bound methods or objects to avoid hitting the database during test collection. The code below will hit the Database while parsing the tests, because this is what Variable.setdefault does when parametrize specification is being parsed - even if test is marked as DB test.

from airflow.models.variable import Variable

pytestmark = pytest.mark.db_test

initial_db_init()


@pytest.mark.parametrize(
    "env, expected",
    [
        pytest.param(
            {"plain_key": "plain_value"},
            "{'plain_key': 'plain_value'}",
            id="env-plain-key-val",
        ),
        pytest.param(
            {"plain_key": Variable.setdefault("plain_var", "banana")},
            "{'plain_key': 'banana'}",
            id="env-plain-key-plain-var",
        ),
        pytest.param(
            {"plain_key": Variable.setdefault("secret_var", "monkey")},
            "{'plain_key': '***'}",
            id="env-plain-key-sensitive-var",
        ),
        pytest.param(
            {"plain_key": "{{ var.value.plain_var }}"},
            "{'plain_key': '{{ var.value.plain_var }}'}",
            id="env-plain-key-plain-tpld-var",
        ),
    ],
)
def test_rendered_task_detail_env_secret(patch_app, admin_client, request, env, expected):
    ...

You can make the code conditional and mock out the Variable to avoid hitting the database.

from airflow.models.variable import Variable

pytestmark = pytest.mark.db_test


if os.environ.get("_AIRFLOW_SKIP_DB_TESTS") == "true":
    # Handle collection of the test by non-db case
    Variable = mock.MagicMock()  # type: ignore[misc] # noqa: F811
else:
    initial_db_init()


@pytest.mark.parametrize(
    "env, expected",
    [
        pytest.param(
            {"plain_key": "plain_value"},
            "{'plain_key': 'plain_value'}",
            id="env-plain-key-val",
        ),
        pytest.param(
            {"plain_key": Variable.setdefault("plain_var", "banana")},
            "{'plain_key': 'banana'}",
            id="env-plain-key-plain-var",
        ),
        pytest.param(
            {"plain_key": Variable.setdefault("secret_var", "monkey")},
            "{'plain_key': '***'}",
            id="env-plain-key-sensitive-var",
        ),
        pytest.param(
            {"plain_key": "{{ var.value.plain_var }}"},
            "{'plain_key': '{{ var.value.plain_var }}'}",
            id="env-plain-key-plain-tpld-var",
        ),
    ],
)
def test_rendered_task_detail_env_secret(patch_app, admin_client, request, env, expected):
    ...

You can also use fixture to create object that needs database just like this.

from airflow.models import Connection

pytestmark = pytest.mark.db_test


@pytest.fixture()
def get_connection1():
    return Connection()


@pytest.fixture()
def get_connection2():
    return Connection(host="apache.org", extra={})


@pytest.mark.parametrize(
    "conn",
    [
        "get_connection1",
        "get_connection2",
    ],
)
def test_as_json_from_connection(self, conn: Connection):
    conn = request.getfixturevalue(conn)
    ...

Running Unit tests

Running Unit Tests from PyCharm IDE

To run unit tests from the PyCharm 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: 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 from PyCharm IDE using Breeze

Ideally, all unit tests should be run using the standardized Breeze environment. While not as convenient as the one-click "play button" in PyCharm, the IDE can be configured to do this in two clicks.

  1. Add Breeze as an "External Tool":
    1. From the settings menu, navigate to Tools > External Tools
    2. Click the little plus symbol to open the "Create Tool" popup and fill it out:

Installing Python extension

  1. Add the tool to the context menu:
    1. From the settings menu, navigate to Appearance & Behavior > Menus & Toolbars > Project View Popup Menu
    2. Click on the list of entries where you would like it to be added. Right above or below "Project View Popup Menu Run Group" may be a good choice, you can drag and drop this list to rearrange the placement later as desired.
    3. Click the little plus at the top of the popup window
    4. Find your "External Tool" in the new "Choose Actions to Add" popup and click OK. If you followed the image above, it will be at External Tools > External Tools > Breeze

Note: That only adds the option to that one menu. If you would like to add it to the context menu when right-clicking on a tab at the top of the editor, for example, follow the steps above again and place it in the "Editor Tab Popup Menu"

Installing Python extension

  1. To run tests in Breeze, right click on the file or directory in the Project View and click Breeze.

Running Unit Tests from Visual Studio Code

To run unit tests from the Visual Studio Code:

  1. Using the Extensions view install Python extension, reload if required

Installing Python extension

  1. Using the Testing view click on Configure Python Tests and select pytest framework

Configuring Python tests

Selecting pytest framework

  1. Open /.vscode/settings.json and add "python.testing.pytestArgs": ["tests"] to enable tests discovery

Enabling tests discovery

  1. Now you are able to run and debug tests from both the Testing view and test files

Running tests

Running Unit Tests in local virtualenv

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

Custom pytest plugin runs 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, they may either fail or provide an error message.

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

pytest tests/core -k "TestCore and not check"

This runs the TestCore class but skips tests of this class that include 'check' in their names. For better performance (due to a test collection), run:

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

This flag is useful when used to run a single test like this:

pytest tests/core/test_core.py -k "test_check_operators"

This can also be done by specifying a full path to the test:

pytest tests/core/test_core.py::TestCore::test_dag_params_and_task_params

To run the whole test class, enter:

pytest tests/core/test_core.py::TestCore

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

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

Running Tests using Breeze interactive shell

You can run tests interactively using regular pytest commands inside the Breeze shell. This has the advantage, that Breeze container has all the dependencies installed that are needed to run the tests and it will ask you to rebuild the image if it is needed and some new dependencies should be installed.

By using interactive shell and iterating over the tests, you can iterate and re-run tests one-by-one or group by group right after you modified them.

Entering the shell is as easy as:

breeze

This should drop you into the container.

You can also use other switches (like --backend for example) to configure the environment for your tests (and for example to switch to different database backend - see --help for more details).

Once you enter the container, you might run regular pytest commands. For example:

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

Running Tests using Breeze from the Host

If you wish to only run tests and not to drop into the shell, apply the tests command. You can add extra targets and pytest flags after the -- command. Note that often you want to run the tests with a clean/reset db, so usually you want to add --db-reset flag to breeze command. The Breeze image usually will have all the dependencies needed and it will ask you to rebuild the image if it is needed and some new dependencies should be installed.

breeze testing tests tests/providers/http/hooks/test_http.py tests/core/test_core.py --db-reset --log-cli-level=DEBUG

You can run the whole test suite without adding the test target:

breeze testing tests --db-reset

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

breeze testing tests --db-reset tests/core/test_core.py::TestCore

You can also limit the tests to execute to specific group of tests

breeze testing tests --test-type Core

In case of Providers tests, you can run tests for all providers

breeze testing tests --test-type Providers

You can limit the set of providers you would like to run tests of

breeze testing tests --test-type "Providers[airbyte,http]"

You can also run all providers but exclude the providers you would like to skip

breeze testing tests --test-type "Providers[-amazon,google]"

Sometimes you need to inspect docker compose after tests command complete, for example when test environment could not be properly set due to failed healthchecks. This can be achieved with --skip-docker-compose-down flag:

breeze testing tests --skip--docker-compose-down

Running full Airflow unit test suite in parallel

If you run breeze testing tests --run-in-parallel tests run in parallel on your development machine - maxing out the number of parallel runs at the number of cores you have available in your Docker engine.

In case you do not have enough memory available to your Docker (8 GB), the Integration. Provider and Core test type are executed sequentially with cleaning the docker setup in-between. This allows to print

This allows for massive speedup in full test execution. On 8 CPU machine with 16 cores and 64 GB memory and fast SSD disk, the whole suite of tests completes in about 5 minutes (!). Same suite of tests takes more than 30 minutes on the same machine when tests are run sequentially.

Note

On MacOS you might have less CPUs and less memory available to run the tests than you have in the host, simply because your Docker engine runs in a Linux Virtual Machine under-the-hood. If you want to make use of the parallelism and memory usage for the CI tests you might want to increase the resources available to your docker engine. See the Resources chapter in the Docker for Mac documentation on how to do it.

You can also limit the parallelism by specifying the maximum number of parallel jobs via MAX_PARALLEL_TEST_JOBS variable. If you set it to "1", all the test types will be run sequentially.

MAX_PARALLEL_TEST_JOBS="1" ./scripts/ci/testing/ci_run_airflow_testing.sh

Note

In case you would like to cleanup after execution of such tests you might have to cleanup some of the docker containers running in case you use ctrl-c to stop execution. You can easily do it by running this command (it will kill all docker containers running so do not use it if you want to keep some docker containers running):

docker kill $(docker ps -q)

Running Backend-Specific Tests

Tests that are using a specific backend are marked with a custom pytest marker pytest.mark.backend. The marker has a single parameter - the name of a backend. It corresponds to the --backend switch of the Breeze environment (one of mysql, sqlite, or postgres). Backend-specific tests only run when the Breeze environment is running with the right backend. If you specify more than one backend in the marker, the test runs for all specified backends.

Example of the postgres only test:

@pytest.mark.backend("postgres")
def test_copy_expert(self):
    ...

Example of the postgres,mysql test (they are skipped with the sqlite backend):

@pytest.mark.backend("postgres", "mysql")
def test_celery_executor(self):
    ...

You can use the custom --backend switch in pytest to only run tests specific for that backend. Here is an example of running only postgres-specific backend tests:

pytest --backend postgres

Running Long-running tests

Some of the tests rung for a long time. Such tests are marked with @pytest.mark.long_running annotation. Those tests are skipped by default. You can enable them with --include-long-running flag. You can also decide to only run tests with -m long-running flags to run only those tests.

Running Quarantined tests

Some of our tests are quarantined. This means that this test will be run in isolation and that it will be re-run several times. Also when quarantined tests fail, the whole test suite will not fail. The quarantined tests are usually flaky tests that need some attention and fix.

Those tests are marked with @pytest.mark.quarantined annotation. Those tests are skipped by default. You can enable them with --include-quarantined flag. You can also decide to only run tests with -m quarantined flag to run only those tests.

Running Tests with provider packages

Airflow 2.0 introduced the concept of splitting the monolithic Airflow package into separate providers packages. The main "apache-airflow" package contains the bare Airflow implementation, and additionally we have 70+ providers that we can install additionally to get integrations with external services. Those providers live in the same monorepo as Airflow, but we build separate packages for them and the main "apache-airflow" package does not contain the providers.

Most of the development in Breeze happens by iterating on sources and when you run your tests during development, you usually do not want to build packages and install them separately. Therefore by default, when you enter Breeze airflow and all providers are available directly from sources rather than installed from packages. This is for example to test the "provider discovery" mechanism available that reads provider information from the package meta-data.

When Airflow is run from sources, the metadata is read from provider.yaml files, but when Airflow is installed from packages, it is read via the package entrypoint apache_airflow_provider.

By default, all packages are prepared in wheel format. To install Airflow from packages you need to run the following steps:

  1. Prepare provider packages
breeze release-management prepare-provider-packages [PACKAGE ...]

If you run this command without packages, you will prepare all packages. However, You can specify providers that you would like to build if you just want to build few provider packages. The packages are prepared in dist folder. Note that this command cleans up the dist folder before running, so you should run it before generating apache-airflow package.

  1. Prepare airflow packages
breeze release-management prepare-airflow-package

This prepares airflow .whl package in the dist folder.

  1. Enter breeze installing both airflow and providers from the dist packages
breeze --use-airflow-version wheel --use-packages-from-dist --mount-sources skip

Code Coverage

Airflow's CI process automatically uploads the code coverage report to codecov.io. For the most recent coverage report of the main branch, visit: https://codecov.io/gh/apache/airflow.

Generating Local Coverage Reports:

If you wish to obtain coverage reports for specific areas of the codebase on your local machine, follow these steps:

  1. Initiate a breeze shell.
  2. Execute one of the commands below based on the desired coverage area:
    • Core: python scripts/cov/core_coverage.py
    • REST API: python scripts/cov/restapi_coverage.py
    • CLI: python scripts/cov/cli_coverage.py
    • Webserver: python scripts/cov/www_coverage.py
  3. After execution, the coverage report will be available at: http://localhost:28000/dev/coverage/index.html.

Note

In order to see the coverage report, you must start webserver first in breeze environment via the airflow webserver. Once you enter breeze, you can start tmux (terminal multiplexer) and split the terminal (by pressing ctrl-B " for example) to continue testing and run the webserver in one terminal and run tests in the second one (you can switch between the terminals with ctrl-B <arrow>).

Modules Not Fully Covered:

Each coverage command provides a list of modules that aren't fully covered. If you wish to enhance coverage for a particular module:

  1. Work on the module to improve its coverage.
  2. Once coverage reaches 100%, you can safely remove the module from the list of modules that are not fully covered. This list is inside each command's source code.

Tracking SQL statements

You can run tests with SQL statements tracking. To do this, use the --trace-sql option and pass the columns to be displayed as an argument. Each query will be displayed on a separate line. Supported values:

  • num - displays the query number;
  • time - displays the query execution time;
  • trace - displays the simplified (one-line) stack trace;
  • sql - displays the SQL statements;
  • parameters - display SQL statement parameters.

If you only provide num, then only the final number of queries will be displayed.

By default, pytest does not display output for successful tests, if you still want to see them, you must pass the --capture=no option.

If you run the following command:

pytest --trace-sql=num,sql,parameters --capture=no \
  tests/jobs/test_scheduler_job.py -k test_process_dags_queries_count_05

On the screen you will see database queries for the given test.

SQL query tracking does not work properly if your test runs subprocesses. Only queries from the main process are tracked.

For other kinds of tests look at Testing document