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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 CI tests. Integration tests are special tests that require additional services running, such as Postgres, MySQL, Kerberos, etc.
  • System tests are automatic tests that use external systems like Google Cloud. These tests are intended for an end-to-end DAG execution. The tests can be executed on both the current version of Apache Airflow and any older versions from 1.10.* series.

This document is about running Python tests. Before the tests are run, use static code checks that enable catching typical errors in the code.

Airflow Unit Tests

All 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.

Note

We are in the process of converting all unit tests to standard "asserts" and pytest fixtures so if you find some tests that are still using classic setUp/tearDown approach or unittest asserts, feel free to convert them to pytest.

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.

Note

Previous way of setting the test configuration

The test configuration for Airflow before July 2023 was automatically generated in a file named AIRFLOW_HOME/unittest.cfg. The template for it was stored in "config_templates" next to the yaml file. However writing the file was only done for the first time you run airflow and you had to manually maintain the file. It was pretty arcane knowledge, and this generated file in {AIRFLOW_HOME} has been overwritten in the Breeze environment with another CI-specific file. Using unit_tests.cfg as a single source of the configuration for tests - coming from Airflow sources rather than from {AIRFLOW_HOME} is much more convenient and it is automatically used by pytest.

The unittest.cfg file generated in {AIRFLOW_HOME} will no longer be used and can be removed.

Airflow test types

Airflow tests in the CI environment are split into several test types:

  • Always - those are tests that should be always executed (always folder)
  • Core - for the core Airflow functionality (core folder)
  • API - Tests for the Airflow API (api and api_connexion folders)
  • CLI - Tests for the Airflow CLI (cli folder)
  • WWW - Tests for the Airflow webserver (www folder)
  • Providers - Tests for all Providers of Airflow (providers folder)
  • Other - all other tests (all other folders that are not part of any of the above)

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 parallel execution of the tests on Self-Hosted runners

For case 2. We can utilise memory and CPUs available on both CI and local development machines to run test in parallel. This way we can decrease the time of running all tests in self-hosted runners from 60 minutes to ~15 minutes.

Note

We need to split tests manually into separate suites rather than utilise pytest-xdist or pytest-parallel which could be a simpler and much more "native" parallelization mechanism. Unfortunately, we cannot utilise those tools because our tests are not truly unit tests that can run in parallel. A lot of our tests rely on shared databases - and they update/reset/cleanup the databases while they are executing. They are also exercising features of the Database such as locking which further increases cross-dependency between tests. Until we make all our tests truly unit tests (and not touching the database or until we isolate all such tests to a separate test type, we cannot really rely on frameworks that run tests in parallel. In our solution each of the test types is run in parallel with its own database (!) so when we have 8 test types running in parallel, there are in fact 8 databases run behind the scenes to support them and each of the test types executes its own tests sequentially.

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_check_operators

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]"

Inspecting docker compose after test commands

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 --skip-mounting-local-sources

Airflow Docker Compose Tests

Running Docker Compose Tests with Breeze

We also test in CI whether the Docker Compose that we expose in our documentation via Running Airflow in Docker works as expected. Those tests are run in CI ("Test docker-compose quick start") and you can run them locally as well.

The way the tests work:

  1. They first build the Airflow production image
  2. Then they take the Docker Compose file of ours and use the image to start it
  3. Then they perform some simple DAG trigger tests which checks whether Airflow is up and can process an example DAG

This is done in a local environment, not in the Breeze CI image. It uses COMPOSE_PROJECT_NAME set to quick-start to avoid conflicts with other docker compose deployments you might have.

The complete test can be performed using Breeze. The prerequisite to that is to have docker-compose (Docker Compose v1) or docker compose plugin (Docker Compose v2) available on the path.

Running complete test with breeze:

breeze prod-image build --python 3.8
breeze testing docker-compose-tests

In case the test fails, it will dump the logs from the running containers to the console and it will shutdown the Docker Compose deployment. In case you want to debug the Docker Compose deployment created for the test, you can pass --skip-docker-compose-deletion flag to Breeze or export SKIP_DOCKER_COMPOSE_DELETION set to "true" variable and the deployment will not be deleted after the test.

You can also specify maximum timeout for the containers with --wait-for-containers-timeout flag. You can also add -s option to the command pass it to underlying pytest command to see the output of the test as it happens (it can be also set via WAIT_FOR_CONTAINERS_TIMEOUT environment variable)

The test can be also run manually with pytest docker_tests/test_docker_compose_quick_start.py command, provided that you have a local airflow venv with dev extra set and the DOCKER_IMAGE environment variable is set to the image you want to test. The variable defaults to ghcr.io/apache/airflow/main/prod/python3.8:latest which is built by default when you run breeze prod-image build --python 3.8. also the switches --skip-docker-compose-deletion and --wait-for-containers-timeout can only be passed via environment variables.

If you want to debug the deployment using docker compose commands after SKIP_DOCKER_COMPOSE_DELETION was used, you should set COMPOSE_PROJECT_NAME to quick-start because this is what the test uses:

export COMPOSE_PROJECT_NAME=quick-start

You can also add --project-name quick-start to the docker compose commands you run. When the test will be re-run it will automatically stop previous deployment and start a new one.

Running Docker Compose deployment manually

You can also (independently of Pytest test) run docker-compose deployment manually with the image you built using the prod image build command above.

export AIRFLOW_IMAGE_NAME=ghcr.io/apache/airflow/main/prod/python3.8:latest

and follow the instructions in the Running Airflow in Docker but make sure to use the docker-compose file from the sources in docs/apache-airflow/stable/howto/docker-compose/ folder.

Then, the usual docker compose and docker commands can be used to debug such running instances. The test performs a simple API call to trigger a DAG and wait for it, but you can follow our documentation to connect to such running docker compose instances and test it manually.

Airflow Integration Tests

Some of the tests in Airflow are integration tests. These tests require airflow Docker image and extra images with integrations (such as celery, mongodb, etc.). The integration tests are all stored in the tests/integration folder.

Enabling Integrations

Airflow integration tests cannot be run in the local virtualenv. They can only run in the Breeze environment with enabled integrations and in the CI. See CI for details about Airflow CI.

When you are in the Breeze environment, by default, all integrations are disabled. This enables only true unit tests to be executed in Breeze. You can enable the integration by passing the --integration <INTEGRATION> switch when starting Breeze. You can specify multiple integrations by repeating the --integration switch or using the --integration all-testable switch that enables all testable integrations and --integration all switch that enables all integrations.

NOTE: Every integration requires a separate container with the corresponding integration image. These containers take precious resources on your PC, mainly the memory. The started integrations are not stopped until you stop the Breeze environment with the stop command and started with the start command.

The following integrations are available:

Airflow Test Integrations
Integration Description
cassandra Integration required for Cassandra hooks
kerberos Integration that provides Kerberos authentication
mongo Integration required for MongoDB hooks
pinot Integration required for Apache Pinot hooks
celery Integration required for Celery executor tests
trino Integration required for Trino hooks

To start the mongo integration only, enter:

breeze --integration mongo

To start mongo and cassandra integrations, enter:

breeze --integration mongo --integration cassandra

To start all testable integrations, enter:

breeze --integration all-testable

To start all integrations, enter:

breeze --integration all-testable

Note that Kerberos is a special kind of integration. Some tests run differently when Kerberos integration is enabled (they retrieve and use a Kerberos authentication token) and differently when the Kerberos integration is disabled (they neither retrieve nor use the token). Therefore, one of the test jobs for the CI system should run all tests with the Kerberos integration enabled to test both scenarios.

Running Integration Tests

All tests using an integration are marked with a custom pytest marker pytest.mark.integration. The marker has a single parameter - the name of integration.

Example of the celery integration test:

@pytest.mark.integration("celery")
def test_real_ping(self):
    hook = RedisHook(redis_conn_id="redis_default")
    redis = hook.get_conn()

    assert redis.ping(), "Connection to Redis with PING works."

The markers can be specified at the test level or the class level (then all tests in this class require an integration). You can add multiple markers with different integrations for tests that require more than one integration.

If such a marked test does not have a required integration enabled, it is skipped. The skip message clearly says what is needed to use the test.

To run all tests with a certain integration, use the custom pytest flag --integration. You can pass several integration flags if you want to enable several integrations at once.

NOTE: If an integration is not enabled in Breeze or CI, the affected test will be skipped.

To run only mongo integration tests:

pytest --integration mongo tests/integration

To run integration tests for mongo and celery:

pytest --integration mongo --integration celery tests/integration

Here is an example of the collection limited to the providers/apache sub-directory:

pytest --integration cassandra tests/integrations/providers/apache

Running Integration Tests from the Host

You can also run integration tests using Breeze from the host.

Runs all integration tests:

breeze testing integration-tests  --db-reset --integration all-testable

Runs all mongo DB tests:

breeze testing integration-tests --db-reset --integration mongo

Helm Unit Tests

On the Airflow Project, we have decided to stick with pythonic testing for our Helm chart. This makes our chart easier to test, easier to modify, and able to run with the same testing infrastructure. To add Helm unit tests add them in helm_tests.

class TestBaseChartTest:
    ...

To render the chart create a YAML string with the nested dictionary of options you wish to test. You can then use our render_chart function to render the object of interest into a testable Python dictionary. Once the chart has been rendered, you can use the render_k8s_object function to create a k8s model object. It simultaneously ensures that the object created properly conforms to the expected resource spec and allows you to use object values instead of nested dictionaries.

Example test here:

from tests.charts.common.helm_template_generator import render_chart, render_k8s_object

git_sync_basic = """
dags:
  gitSync:
  enabled: true
"""


class TestGitSyncScheduler:
    def test_basic(self):
        helm_settings = yaml.safe_load(git_sync_basic)
        res = render_chart(
            "GIT-SYNC",
            helm_settings,
            show_only=["templates/scheduler/scheduler-deployment.yaml"],
        )
        dep: k8s.V1Deployment = render_k8s_object(res[0], k8s.V1Deployment)
        assert "dags" == dep.spec.template.spec.volumes[1].name

To execute all Helm tests using breeze command and utilize parallel pytest tests, you can run the following command (but it takes quite a long time even in a multi-processor machine).

breeze testing helm-tests

You can also execute tests from a selected package only. Tests in tests/chart are grouped by packages so rather than running all tests, you can run only tests from a selected package. For example:

breeze testing helm-tests --helm-test-package basic

Will run all tests from tests-charts/basic package.

You can also run Helm tests individually via the usual breeze command. Just enter breeze and run the tests with pytest as you would do with regular unit tests (you can add -n auto command to run Helm tests in parallel - unlike most of the regular unit tests of ours that require a database, the Helm tests are perfectly safe to be run in parallel (and if you have multiple processors, you can gain significant speedups when using parallel runs):

breeze

This enters breeze container.

pytest helm_tests -n auto

This runs all chart tests using all processors you have available.

pytest helm_tests/test_airflow_common.py -n auto

This will run all tests from tests_airflow_common.py file using all processors you have available.

pytest helm_tests/test_airflow_common.py

This will run all tests from tests_airflow_common.py file sequentially.

Kubernetes tests

Airflow has tests that are run against real Kubernetes cluster. We are using Kind to create and run the cluster. We integrated the tools to start/stop/ deploy and run the cluster tests in our repository and into Breeze development environment.

KinD has a really nice kind tool that you can use to interact with the cluster. Run kind --help to learn more.

K8S test environment

Before running breeze k8s cluster commands you need to setup the environment. This is done by breeze k8s setup-env command. Breeze in this command makes sure to download tools that are needed to run k8s tests: Helm, Kind, Kubectl in the right versions and sets up a Python virtualenv that is needed to run the tests. All those tools and env are setup in .build/.k8s-env folder. You can activate this environment yourselves as usual by sourcing bin/activate script, but since we are supporting multiple clusters in the same installation it is best if you use breeze k8s shell with the right parameters specifying which cluster to use.

Multiple cluster support

The main feature of breeze k8s command is that it allows you to manage multiple KinD clusters - one per each combination of Python and Kubernetes version. This is used during CI where we can run same tests against those different clusters - even in parallel.

The cluster name follows the pattern airflow-python-X.Y-vA.B.C where X.Y is a major/minor Python version and A.B.C is Kubernetes version. Example cluster name: airflow-python-3.8-v1.24.0

Most of the commands can be executed in parallel for multiple images/clusters by adding --run-in-parallel to create clusters or deploy airflow. Similarly checking for status, dumping logs and deleting clusters can be run with --all flag and they will be executed sequentially for all locally created clusters.

Per-cluster configuration files

Once you start the cluster, the configuration for it is stored in a dynamically created folder - separate folder for each python/kubernetes_version combination. The folder is ./build/.k8s-clusters/<CLUSTER_NAME>

There are two files there:

  • kubectl config file stored in .kubeconfig file - our scripts set the KUBECONFIG variable to it
  • KinD cluster configuration in .kindconfig.yml file - our scripts set the KINDCONFIG variable to it

The KUBECONFIG file is automatically used when you enter any of the breeze k8s commands that use kubectl or when you run kubectl in the k8s shell. The KINDCONFIG file is used when cluster is started but You and the k8s command can inspect it to know for example what port is forwarded to the webserver running in the cluster.

The files are deleted by breeze k8s delete-cluster command.

Managing Kubernetes Cluster

For your testing, you manage Kind cluster with k8s breeze command group. Those commands allow to created:

Breeze k8s

The command group allows you to setup environment, start/stop/recreate/status Kind Kubernetes cluster, configure cluster (via create-cluster, configure-cluster command). Those commands can be run with --run-in-parallel flag for all/selected clusters and they can be executed in parallel.

In order to deploy Airflow, the PROD image of Airflow need to be extended and example dags and POD template files should be added to the image. This is done via build-k8s-image, upload-k8s-image. This can also be done for all/selected images/clusters in parallel via --run-in-parallel flag.

Then Airflow (by using Helm Chart) can be deployed to the cluster via deploy-airflow command. This can also be done for all/selected images/clusters in parallel via --run-in-parallel flag. You can pass extra options when deploying airflow to configure your depliyment.

You can check the status, dump logs and finally delete cluster via status, logs, delete-cluster commands. This can also be done for all created clusters in parallel via --all flag.

You can interact with the cluster (via shell and k9s commands).

You can run set of k8s tests via tests command. You can also run tests in parallel on all/selected clusters by --run-in-parallel flag.

Running tests with Kubernetes Cluster

You can either run all tests or you can select which tests to run. You can also enter interactive virtualenv to run the tests manually one by one.

Running Kubernetes tests via breeze:

breeze k8s tests
breeze k8s tests TEST TEST [TEST ...]

Optionally add --executor:

breeze k8s tests --executor CeleryExecutor
breeze k8s tests --executor CeleryExecutor TEST TEST [TEST ...]

Entering shell with Kubernetes Cluster

This shell is prepared to run Kubernetes tests interactively. It has kubectl and kind cli tools available in the path, it has also activated virtualenv environment that allows you to run tests via pytest.

The virtualenv is available in ./.build/.k8s-env/ The binaries are available in .build/.k8s-env/bin path.

breeze k8s shell

Optionally add --executor:

breeze k8s shell --executor CeleryExecutor

K9s CLI - debug Kubernetes in style!

Breeze has built-in integration with fantastic k9s CLI tool, that allows you to debug the Kubernetes installation effortlessly and in style. K9S provides terminal (but windowed) CLI that helps you to:

  • easily observe what's going on in the Kubernetes cluster
  • observe the resources defined (pods, secrets, custom resource definitions)
  • enter shell for the Pods/Containers running,
  • see the log files and more.

You can read more about k9s at https://k9scli.io/

Here is the screenshot of k9s tools in operation:

K9S tool

You can enter the k9s tool via breeze (after you deployed Airflow):

breeze k8s k9s

You can exit k9s by pressing Ctrl-C.

Typical testing pattern for Kubernetes tests

The typical session for tests with Kubernetes looks like follows:

  1. Prepare the environment:
breeze k8s setup-env

The first time you run it, it should result in creating the virtualenv and installing good versions of kind, kubectl and helm. All of them are installed in ./build/.k8s-env (binaries available in bin sub-folder of it).

Initializing K8S virtualenv in /Users/jarek/IdeaProjects/airflow/.build/.k8s-env
Reinstalling PIP version in /Users/jarek/IdeaProjects/airflow/.build/.k8s-env
Installing necessary packages in /Users/jarek/IdeaProjects/airflow/.build/.k8s-env
The ``kind`` tool is not downloaded yet. Downloading 0.14.0 version.
Downloading from: https://github.com/kubernetes-sigs/kind/releases/download/v0.14.0/kind-darwin-arm64
The ``kubectl`` tool is not downloaded yet. Downloading 1.24.3 version.
Downloading from: https://storage.googleapis.com/kubernetes-release/release/v1.24.3/bin/darwin/arm64/kubectl
The ``helm`` tool is not downloaded yet. Downloading 3.9.2 version.
Downloading from: https://get.helm.sh/helm-v3.9.2-darwin-arm64.tar.gz
Extracting the darwin-arm64/helm to /Users/jarek/IdeaProjects/airflow/.build/.k8s-env/bin
Moving the helm to /Users/jarek/IdeaProjects/airflow/.build/.k8s-env/bin/helm

This prepares the virtual environment for tests and downloads the right versions of the tools to ./build/.k8s-env

  1. Create the KinD cluster:
breeze k8s create-cluster

Should result in KinD creating the K8S cluster.

Config created in /Users/jarek/IdeaProjects/airflow/.build/.k8s-clusters/airflow-python-3.8-v1.24.2/.kindconfig.yaml:

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
---
kind: Cluster
apiVersion: kind.x-k8s.io/v1alpha4
networking:
  ipFamily: ipv4
  apiServerAddress: "127.0.0.1"
  apiServerPort: 48366
nodes:
  - role: control-plane
  - role: worker
    extraPortMappings:
      - containerPort: 30007
        hostPort: 18150
        listenAddress: "127.0.0.1"
        protocol: TCP



Creating cluster "airflow-python-3.8-v1.24.2" ...
 ✓ Ensuring node image (kindest/node:v1.24.2) 🖼
 ✓ Preparing nodes 📦 📦
 ✓ Writing configuration 📜
 ✓ Starting control-plane 🕹️
 ✓ Installing CNI 🔌
 ✓ Installing StorageClass 💾
 ✓ Joining worker nodes 🚜
Set kubectl context to "kind-airflow-python-3.8-v1.24.2"
You can now use your cluster with:

kubectl cluster-info --context kind-airflow-python-3.8-v1.24.2

Not sure what to do next? 😅  Check out https://kind.sigs.k8s.io/docs/user/quick-start/

KinD Cluster API server URL: http://localhost:48366
Connecting to localhost:18150. Num try: 1
Error when connecting to localhost:18150 : ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))

Airflow webserver is not available at port 18150. Run `breeze k8s deploy-airflow --python 3.8 --kubernetes-version v1.24.2` to (re)deploy airflow

KinD cluster airflow-python-3.8-v1.24.2 created!

NEXT STEP: You might now configure your cluster by:

breeze k8s configure-cluster
  1. Configure cluster for Airflow - this will recreate namespace and upload test resources for Airflow.
breeze k8s configure-cluster
Configuring airflow-python-3.8-v1.24.2 to be ready for Airflow deployment
Deleting K8S namespaces for kind-airflow-python-3.8-v1.24.2
Error from server (NotFound): namespaces "airflow" not found
Error from server (NotFound): namespaces "test-namespace" not found
Creating namespaces
namespace/airflow created
namespace/test-namespace created
Created K8S namespaces for cluster kind-airflow-python-3.8-v1.24.2

Deploying test resources for cluster kind-airflow-python-3.8-v1.24.2
persistentvolume/test-volume created
persistentvolumeclaim/test-volume created
service/airflow-webserver-node-port created
Deployed test resources for cluster kind-airflow-python-3.8-v1.24.2


NEXT STEP: You might now build your k8s image by:

breeze k8s build-k8s-image
  1. Check the status of the cluster
breeze k8s status

Should show the status of current KinD cluster.

========================================================================================================================
Cluster: airflow-python-3.8-v1.24.2

    * KUBECONFIG=/Users/jarek/IdeaProjects/airflow/.build/.k8s-clusters/airflow-python-3.8-v1.24.2/.kubeconfig
    * KINDCONFIG=/Users/jarek/IdeaProjects/airflow/.build/.k8s-clusters/airflow-python-3.8-v1.24.2/.kindconfig.yaml

Cluster info: airflow-python-3.8-v1.24.2

Kubernetes control plane is running at https://127.0.0.1:48366
CoreDNS is running at https://127.0.0.1:48366/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy

To further debug and diagnose cluster problems, use 'kubectl cluster-info dump'.

Storage class for airflow-python-3.8-v1.24.2

NAME                 PROVISIONER             RECLAIMPOLICY   VOLUMEBINDINGMODE      ALLOWVOLUMEEXPANSION   AGE
standard (default)   rancher.io/local-path   Delete          WaitForFirstConsumer   false                  83s

Running pods for airflow-python-3.8-v1.24.2

NAME                                                               READY   STATUS    RESTARTS   AGE
coredns-6d4b75cb6d-rwp9d                                           1/1     Running   0          71s
coredns-6d4b75cb6d-vqnrc                                           1/1     Running   0          71s
etcd-airflow-python-3.8-v1.24.2-control-plane                      1/1     Running   0          84s
kindnet-ckc8l                                                      1/1     Running   0          69s
kindnet-qqt8k                                                      1/1     Running   0          71s
kube-apiserver-airflow-python-3.8-v1.24.2-control-plane            1/1     Running   0          84s
kube-controller-manager-airflow-python-3.8-v1.24.2-control-plane   1/1     Running   0          84s
kube-proxy-6g7hn                                                   1/1     Running   0          69s
kube-proxy-dwfvp                                                   1/1     Running   0          71s
kube-scheduler-airflow-python-3.8-v1.24.2-control-plane            1/1     Running   0          84s

KinD Cluster API server URL: http://localhost:48366
Connecting to localhost:18150. Num try: 1
Error when connecting to localhost:18150 : ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))

Airflow webserver is not available at port 18150. Run `breeze k8s deploy-airflow --python 3.8 --kubernetes-version v1.24.2` to (re)deploy airflow


Cluster healthy: airflow-python-3.8-v1.24.2
  1. Build the image base on PROD Airflow image. You need to build the PROD image first (the command will guide you if you did not - either by running the build separately or passing --rebuild-base-image flag
breeze k8s build-k8s-image
Building the K8S image for Python 3.8 using airflow base image: ghcr.io/apache/airflow/main/prod/python3.8:latest

[+] Building 0.1s (8/8) FINISHED
 => [internal] load build definition from Dockerfile                                                                                                                                                                                                                                           0.0s
 => => transferring dockerfile: 301B                                                                                                                                                                                                                                                           0.0s
 => [internal] load .dockerignore                                                                                                                                                                                                                                                              0.0s
 => => transferring context: 35B                                                                                                                                                                                                                                                               0.0s
 => [internal] load metadata for ghcr.io/apache/airflow/main/prod/python3.8:latest                                                                                                                                                                                                             0.0s
 => [1/3] FROM ghcr.io/apache/airflow/main/prod/python3.8:latest                                                                                                                                                                                                                               0.0s
 => [internal] load build context                                                                                                                                                                                                                                                              0.0s
 => => transferring context: 3.00kB                                                                                                                                                                                                                                                            0.0s
 => CACHED [2/3] COPY airflow/example_dags/ /opt/airflow/dags/                                                                                                                                                                                                                                 0.0s
 => CACHED [3/3] COPY airflow/kubernetes_executor_templates/ /opt/airflow/pod_templates/                                                                                                                                                                                                       0.0s
 => exporting to image                                                                                                                                                                                                                                                                         0.0s
 => => exporting layers                                                                                                                                                                                                                                                                        0.0s
 => => writing image sha256:c0bdd363c549c3b0731b8e8ce34153d081f239ee2b582355b7b3ffd5394c40bb                                                                                                                                                                                                   0.0s
 => => naming to ghcr.io/apache/airflow/main/prod/python3.8-kubernetes:latest

NEXT STEP: You might now upload your k8s image by:

breeze k8s upload-k8s-image
  1. Upload the image to KinD cluster - this uploads your image to make it available for the KinD cluster.
breeze k8s upload-k8s-image
K8S Virtualenv is initialized in /Users/jarek/IdeaProjects/airflow/.build/.k8s-env
Good version of kind installed: 0.14.0 in /Users/jarek/IdeaProjects/airflow/.build/.k8s-env/bin
Good version of kubectl installed: 1.25.0 in /Users/jarek/IdeaProjects/airflow/.build/.k8s-env/bin
Good version of helm installed: 3.9.2 in /Users/jarek/IdeaProjects/airflow/.build/.k8s-env/bin
Stable repo is already added
Uploading Airflow image ghcr.io/apache/airflow/main/prod/python3.8-kubernetes to cluster airflow-python-3.8-v1.24.2
Image: "ghcr.io/apache/airflow/main/prod/python3.8-kubernetes" with ID "sha256:fb6195f7c2c2ad97788a563a3fe9420bf3576c85575378d642cd7985aff97412" not yet present on node "airflow-python-3.8-v1.24.2-worker", loading...
Image: "ghcr.io/apache/airflow/main/prod/python3.8-kubernetes" with ID "sha256:fb6195f7c2c2ad97788a563a3fe9420bf3576c85575378d642cd7985aff97412" not yet present on node "airflow-python-3.8-v1.24.2-control-plane", loading...

NEXT STEP: You might now deploy airflow by:

breeze k8s deploy-airflow
  1. Deploy Airflow to the cluster - this will use Airflow Helm Chart to deploy Airflow to the cluster.
breeze k8s deploy-airflow
Deploying Airflow for cluster airflow-python-3.8-v1.24.2
Deploying kind-airflow-python-3.8-v1.24.2 with airflow Helm Chart.
Copied chart sources to /private/var/folders/v3/gvj4_mw152q556w2rrh7m46w0000gn/T/chart_edu__kir/chart
Deploying Airflow from /private/var/folders/v3/gvj4_mw152q556w2rrh7m46w0000gn/T/chart_edu__kir/chart
NAME: airflow
LAST DEPLOYED: Tue Aug 30 22:57:54 2022
NAMESPACE: airflow
STATUS: deployed
REVISION: 1
TEST SUITE: None
NOTES:
Thank you for installing Apache Airflow 2.3.4!

Your release is named airflow.
You can now access your dashboard(s) by executing the following command(s) and visiting the corresponding port at localhost in your browser:

Airflow Webserver:     kubectl port-forward svc/airflow-webserver 8080:8080 --namespace airflow
Default Webserver (Airflow UI) Login credentials:
    username: admin
    password: admin
Default Postgres connection credentials:
    username: postgres
    password: postgres
    port: 5432

You can get Fernet Key value by running the following:

    echo Fernet Key: $(kubectl get secret --namespace airflow airflow-fernet-key -o jsonpath="{.data.fernet-key}" | base64 --decode)

WARNING:
    Kubernetes workers task logs may not persist unless you configure log persistence or remote logging!
    Logging options can be found at: https://airflow.apache.org/docs/helm-chart/stable/manage-logs.html
    (This warning can be ignored if logging is configured with environment variables or secrets backend)

###########################################################
#  WARNING: You should set a static webserver secret key  #
###########################################################

You are using a dynamically generated webserver secret key, which can lead to
unnecessary restarts of your Airflow components.

Information on how to set a static webserver secret key can be found here:
https://airflow.apache.org/docs/helm-chart/stable/production-guide.html#webserver-secret-key
Deployed kind-airflow-python-3.8-v1.24.2 with airflow Helm Chart.

Airflow for Python 3.8 and K8S version v1.24.2 has been successfully deployed.

The KinD cluster name: airflow-python-3.8-v1.24.2
The kubectl cluster name: kind-airflow-python-3.8-v1.24.2.


KinD Cluster API server URL: http://localhost:48366
Connecting to localhost:18150. Num try: 1
Established connection to webserver at http://localhost:18150/health and it is healthy.
Airflow Web server URL: http://localhost:18150 (admin/admin)

NEXT STEP: You might now run tests or interact with airflow via shell (kubectl, pytest etc.) or k9s commands:


breeze k8s tests

breeze k8s shell

breeze k8s k9s
  1. Run Kubernetes tests

Note that the tests are executed in production container not in the CI container. There is no need for the tests to run inside the Airflow CI container image as they only communicate with the Kubernetes-run Airflow deployed via the production image. Those Kubernetes tests require virtualenv to be created locally with airflow installed. The virtualenv required will be created automatically when the scripts are run.

8a) You can run all the tests

breeze k8s tests
Running tests with kind-airflow-python-3.8-v1.24.2 cluster.
 Command to run: pytest kubernetes_tests
========================================================================================= test session starts ==========================================================================================
platform darwin -- Python 3.9.9, pytest-6.2.5, py-1.11.0, pluggy-1.0.0 -- /Users/jarek/IdeaProjects/airflow/.build/.k8s-env/bin/python
cachedir: .pytest_cache
rootdir: /Users/jarek/IdeaProjects/airflow/kubernetes_tests
plugins: anyio-3.6.1, instafail-0.4.2, xdist-2.5.0, forked-1.4.0, timeouts-1.2.1, cov-3.0.0
setup timeout: 0.0s, execution timeout: 0.0s, teardown timeout: 0.0s
collected 55 items

test_kubernetes_executor.py::TestKubernetesExecutor::test_integration_run_dag PASSED                                                                                            [  1%]
test_kubernetes_executor.py::TestKubernetesExecutor::test_integration_run_dag_with_scheduler_failure PASSED                                                                     [  3%]
test_kubernetes_pod_operator.py::TestKubernetesPodOperatorSystem::test_already_checked_on_failure PASSED                                                                        [  5%]
test_kubernetes_pod_operator.py::TestKubernetesPodOperatorSystem::test_already_checked_on_success   ...

8b) You can enter an interactive shell to run tests one-by-one

This enters the virtualenv in .build/.k8s-env folder:

breeze k8s shell

Once you enter the environment, you receive this information:

Entering interactive k8s shell.

(kind-airflow-python-3.8-v1.24.2:KubernetesExecutor)>

In a separate terminal you can open the k9s CLI:

breeze k8s k9s

Use it to observe what's going on in your cluster.

  1. Debugging in IntelliJ/PyCharm

It is very easy to running/debug Kubernetes tests with IntelliJ/PyCharm. Unlike the regular tests they are in kubernetes_tests folder and if you followed the previous steps and entered the shell using breeze k8s shell command, you can setup your IDE very easy to run (and debug) your tests using the standard IntelliJ Run/Debug feature. You just need a few steps:

9a) Add the virtualenv as interpreter for the project:

Kubernetes testing virtualenv

The virtualenv is created in your "Airflow" source directory in the .build/.k8s-env folder and you have to find python binary and choose it when selecting interpreter.

9b) Choose pytest as test runner:

Pytest runner

9c) Run/Debug tests using standard "Run/Debug" feature of IntelliJ

Run/Debug tests

NOTE! The first time you run it, it will likely fail with kubernetes.config.config_exception.ConfigException: Invalid kube-config file. Expected key current-context in kube-config. You need to add KUBECONFIG environment variable copying it from the result of "breeze k8s tests":

echo ${KUBECONFIG}

/home/jarek/code/airflow/.build/.kube/config

Run/Debug tests

The configuration for Kubernetes is stored in your "Airflow" source directory in ".build/.kube/config" file and this is where KUBECONFIG env should point to.

You can iterate with tests while you are in the virtualenv. All the tests requiring Kubernetes cluster are in "kubernetes_tests" folder. You can add extra pytest parameters then (for example -s will print output generated test logs and print statements to the terminal immediately. You should have kubernetes_tests as your working directory.

pytest test_kubernetes_executor.py::TestKubernetesExecutor::test_integration_run_dag_with_scheduler_failure -s

You can modify the tests or KubernetesPodOperator and re-run them without re-deploying Airflow to KinD cluster.

  1. Dumping logs

Sometimes You want to see the logs of the clister. This can be done with breeze k8s logs.

breeze k8s logs
  1. Redeploying airflow

Sometimes there are side effects from running tests. You can run breeze k8s deploy-airflow --upgrade without recreating the whole cluster.

breeze k8s deploy-airflow --upgrade

If needed you can also delete the cluster manually (within the virtualenv activated by breeze k8s shell:

kind get clusters
kind delete clusters <NAME_OF_THE_CLUSTER>

Kind has also useful commands to inspect your running cluster:

kind --help
  1. Stop KinD cluster when you are done
breeze k8s delete-cluster
Deleting KinD cluster airflow-python-3.8-v1.24.2!
Deleting cluster "airflow-python-3.8-v1.24.2" ...
KinD cluster airflow-python-3.8-v1.24.2 deleted!

Running complete k8s tests

You can also run complete k8s tests with

breeze k8s run-complete-tests

This will create cluster, build images, deploy airflow run tests and finally delete clusters as single command. It is the way it is run in our CI, you can also run such complete tests in parallel.

Manually testing release candidate packages

Breeze can be used to test new release candidates of packages - both Airflow and providers. You can easily turn the CI image of Breeze to install and start Airflow for both Airflow and provider packages - both, packages that are built from sources and packages that are downloaded from PyPI when they are released there as release candidates.

The way to test it is rather straightforward:

  1. Make sure that the packages - both airflow and providers are placed in the dist folder of your Airflow source tree. You can either build them there or download from PyPI (see the next chapter)
  2. You can run `breeze shell or breeze start-airflow commands with adding the following flags - --mount-sources remove and --use-packages-from-dist. The first one removes the airflow source tree from the container when starting it, the second one installs airflow and providers packages from the dist folder when entering breeze.

Testing pre-release packages

There are two ways how you can get Airflow packages in dist folder - by building them from sources or downloading them from PyPI.

Note

Make sure you run rm dist/* before you start building packages or downloading them from PyPI because the packages built there already are not removed manually.

In order to build apache-airflow from sources, you need to run the following command:

breeze release-management prepare-airflow-package

In order to build providers from sources, you need to run the following command:

breeze release-management prepare-provider-packages <PROVIDER_1> <PROVIDER_2> ... <PROVIDER_N>

The packages are built in dist folder and the command will summarise what packages are available in the dist folder after it finishes.

If you want to download the packages from PyPI, you need to run the following command:

pip download apache-airflow-providers-<PROVIDER_NAME>==X.Y.Zrc1 --dest dist --no-deps

You can use it for both release and pre-release packages.

Examples of testing pre-release packages

Few examples below explain how you can test pre-release packages, and combine them with locally build and released packages.

The following example downloads apache-airflow and celery and kubernetes provider packages from PyPI and eventually starts Airflow with the Celery Executor. It also loads example dags and default connections:

rm dist/*
pip download apache-airflow==2.7.0rc1 --dest dist --no-deps
pip download apache-airflow-providers-cncf-kubernetes==7.4.0rc1 --dest dist --no-deps
pip download apache-airflow-providers-cncf-kubernetes==3.3.0rc1 --dest dist --no-deps
breeze start-airflow --mount-sources remove --use-packages-from-dist --executor CeleryExecutor --load-default-connections --load-example-dags

The following example downloads celery and kubernetes provider packages from PyPI, builds apache-airflow package from the main sources and eventually starts Airflow with the Celery Executor. It also loads example dags and default connections:

rm dist/*
breeze release-management prepare-airflow-package
pip download apache-airflow-providers-cncf-kubernetes==7.4.0rc1 --dest dist --no-deps
pip download apache-airflow-providers-cncf-kubernetes==3.3.0rc1 --dest dist --no-deps
breeze start-airflow --mount-sources remove --use-packages-from-dist --executor CeleryExecutor --load-default-connections --load-example-dags

The following example builds celery, kubernetes provider packages from PyPI, downloads 2.6.3 version of apache-airflow package from PyPI and eventually starts Airflow using default executor for the backend chosen (no example dags, no default connections):

rm dist/*
pip download apache-airflow==2.6.3 --dest dist --no-deps
breeze release-management prepare-provider-packages celery cncf.kubernetes
breeze start-airflow --mount-sources remove --use-packages-from-dist

You can mix and match packages from PyPI (final or pre-release candidates) with locally build packages. You can also choose which providers to install this way since the --remove-sources flag makes sure that Airflow installed does not contain all the providers - only those that you explicitly downloaded or built in the dist folder. This way you can test all the combinations of Airflow + Providers you might need.

Airflow System Tests

System tests need to communicate with external services/systems that are available if you have appropriate credentials configured for your tests. The system tests derive from the tests.test_utils.system_test_class.SystemTests class. They should also be marked with @pytest.marker.system(SYSTEM) where system designates the system to be tested (for example, google.cloud). These tests are skipped by default.

You can execute the system tests by providing the --system SYSTEM flag to pytest. You can specify several --system flags if you want to execute tests for several systems.

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

See more details about adding new system tests below.

Environment for System Tests

Prerequisites: You may need to set some variables to run system tests. If you need to add some initialization of environment variables to Breeze, you can add a variables.env file in the files/airflow-breeze-config/variables.env file. It will be automatically sourced when entering the Breeze environment. You can also add some additional initialization commands in this file if you want to execute something always at the time of entering Breeze.

There are several typical operations you might want to perform such as:

  • generating a file with the random value used across the whole Breeze session (this is useful if you want to use this random number in names of resources that you create in your service
  • generate variables that will be used as the name of your resources
  • decrypt any variables and resources you keep as encrypted in your configuration files
  • install additional packages that are needed in case you are doing tests with 1.10.* Airflow series (see below)

Example variables.env file is shown here (this is part of the variables.env file that is used to run Google Cloud system tests.

# Build variables. This file is sourced by Breeze.
# Also it is sourced during continuous integration build in Cloud Build

# Auto-export all variables
set -a

echo
echo "Reading variables"
echo

# Generate random number that will be used across your session
RANDOM_FILE="/random.txt"

if [[ ! -f "${RANDOM_FILE}" ]]; then
    echo "${RANDOM}" > "${RANDOM_FILE}"
fi

RANDOM_POSTFIX=$(cat "${RANDOM_FILE}")

To execute system tests, specify the --system SYSTEM flag where SYSTEM is a system to run the system tests for. It can be repeated.

Forwarding Authentication from the Host

For system tests, you can also forward authentication from the host to your Breeze container. You can specify the --forward-credentials flag when starting Breeze. Then, it will also forward the most commonly used credentials stored in your home directory. Use this feature with care as it makes your personal credentials visible to anything that you have installed inside the Docker container.

Currently forwarded credentials are:
  • credentials stored in ${HOME}/.aws for aws - Amazon Web Services client
  • credentials stored in ${HOME}/.azure for az - Microsoft Azure client
  • credentials stored in ${HOME}/.config for gcloud - Google Cloud client (among others)
  • credentials stored in ${HOME}/.docker for docker client
  • credentials stored in ${HOME}/.snowsql for snowsql - SnowSQL (Snowflake CLI client)

Adding a New System Test

We are working on automating system tests execution (AIP-4) but for now, system tests are skipped when tests are run in our CI system. But to enable the test automation, we encourage you to add system tests whenever an operator/hook/sensor is added/modified in a given system.

  • To add your own system tests, derive them from the tests.test_utils.system_tests_class.SystemTest class and mark with the @pytest.mark.system(SYSTEM_NAME) marker. The system name should follow the path defined in the providers package (for example, the system tests from tests.providers.google.cloud package should be marked with @pytest.mark.system("google.cloud").
  • If your system tests need some credential files to be available for an authentication with external systems, make sure to keep these credentials in the files/airflow-breeze-config/keys directory. Mark your tests with @pytest.mark.credential_file(<FILE>) so that they are skipped if such a credential file is not there. The tests should read the right credentials and authenticate them on their own. The credentials are read in Breeze from the /files directory. The local "files" folder is mounted to the "/files" folder in Breeze.
  • If your system tests are long-running ones (i.e., require more than 20-30 minutes to complete), mark them with the `@pytest.markers.long_running marker. Such tests are skipped by default unless you specify the --long-running flag to pytest.
  • The system test itself (python class) does not have any logic. Such a test runs the DAG specified by its ID. This DAG should contain the actual DAG logic to execute. Make sure to define the DAG in providers/<SYSTEM_NAME>/example_dags. These example DAGs are also used to take some snippets of code out of them when documentation is generated. So, having these DAGs runnable is a great way to make sure the documentation is describing a working example. Inside your test class/test method, simply use self.run_dag(<DAG_ID>,<DAG_FOLDER>) to run the DAG. Then, the system class will take care about running the DAG. Note that the DAG_FOLDER should be a subdirectory of the tests.test_utils.AIRFLOW_MAIN_FOLDER + providers/<SYSTEM_NAME>/example_dags.

A simple example of a system test is available in:

tests/providers/google/cloud/operators/test_compute_system.py.

It runs two DAGs defined in airflow.providers.google.cloud.example_dags.example_compute.py.

The typical system test session

Here is the typical session that you need to do to run system tests:

  1. Enter breeze
breeze down
breeze --python 3.8 --db-reset --forward-credentials

This will:

  • stop the whole environment (i.e. recreates metadata database from the scratch)
  • run Breeze with: * python 3.8 version * resetting the Airflow database * forward your local credentials to Breeze
  1. Run the tests:
pytest -o faulthandler_timeout=2400 \
   --system=google tests/providers/google/cloud/operators/test_compute_system.py

Iteration with System Tests if your resources are slow to create

When you want to iterate on system tests, you might want to create slow resources first.

If you need to set up some external resources for your tests (for example compute instances in Google Cloud) you should set them up and teardown in the setUp/tearDown methods of your tests. Since those resources might be slow to create, you might want to add some helpers that set them up and tear them down separately via manual operations. This way you can iterate on the tests without waiting for setUp and tearDown with every test.

In this case, you should build in a mechanism to skip setUp and tearDown in case you manually created the resources. A somewhat complex example of that can be found in tests.providers.google.cloud.operators.test_cloud_sql_system.py and the helper is available in tests.providers.google.cloud.operators.test_cloud_sql_system_helper.py.

When the helper is run with --action create to create cloud sql instances which are very slow to create and set-up so that you can iterate on running the system tests without losing the time for creating theme every time. A temporary file is created to prevent from setting up and tearing down the instances when running the test.

This example also shows how you can use the random number generated at the entry of Breeze if you have it in your variables.env (see the previous chapter). In the case of Cloud SQL, you cannot reuse the same instance name for a week so we generate a random number that is used across the whole session and store it in /random.txt file so that the names are unique during tests.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Important !!!!!!!!!!!!!!!!!!!!!!!!!!!!

Do not forget to delete manually created resources before leaving the Breeze session. They are usually expensive to run.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Important !!!!!!!!!!!!!!!!!!!!!!!!!!!!

  1. Enter breeze
breeze down
breeze --python 3.8 --db-reset --forward-credentials
  1. Run create action in helper (to create slowly created resources):
python tests/providers/google/cloud/operators/test_cloud_sql_system_helper.py --action create
  1. Run the tests:
pytest -o faulthandler_timeout=2400 \
   --system=google tests/providers/google/cloud/operators/test_compute_system.py
  1. Run delete action in helper:
python tests/providers/google/cloud/operators/test_cloud_sql_system_helper.py --action delete

Local and Remote Debugging in IDE

One of the great benefits of using the local virtualenv and Breeze is an option to run local debugging in your IDE graphical interface.

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 the 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

Setup VM on GCP with SSH forwarding

Below are the steps you need to take to set up your virtual machine in the Google Cloud.

  1. The next steps will assume that you have configured environment variables with the name of the network and a virtual machine, project ID and the zone where the virtual machine will be created

    PROJECT_ID="<PROJECT_ID>"
GCP_ZONE="europe-west3-a"
GCP_NETWORK_NAME="airflow-debugging"
GCP_INSTANCE_NAME="airflow-debugging-ci"

  2. It is necessary to configure the network and firewall for your machine. The firewall must have unblocked access to port 22 for SSH traffic and any other port for the debugger. In the example for the debugger, we will use port 5555.

    gcloud compute --project="${PROJECT_ID}" networks create "${GCP_NETWORK_NAME}" \
  --subnet-mode=auto

gcloud compute --project="${PROJECT_ID}" firewall-rules create "${GCP_NETWORK_NAME}-allow-ssh" \
  --network "${GCP_NETWORK_NAME}" \
  --allow tcp:22 \
  --source-ranges 0.0.0.0/0

gcloud compute --project="${PROJECT_ID}" firewall-rules create "${GCP_NETWORK_NAME}-allow-debugger" \
  --network "${GCP_NETWORK_NAME}" \
  --allow tcp:5555 \
  --source-ranges 0.0.0.0/0

  3. If you have a network, you can create a virtual machine. To save costs, you can create a Preemptible virtual machine <https://cloud.google.com/preemptible-vms> that is automatically deleted for up to 24 hours.

    gcloud beta compute --project="${PROJECT_ID}" instances create "${GCP_INSTANCE_NAME}" \
  --zone="${GCP_ZONE}" \
  --machine-type=f1-micro \
  --subnet="${GCP_NETWORK_NAME}" \
  --image=debian-11-bullseye-v20220120 \
  --image-project=debian-cloud \
  --preemptible

    To check the public IP address of the machine, you can run the command

    gcloud compute --project="${PROJECT_ID}" instances describe "${GCP_INSTANCE_NAME}" \
  --zone="${GCP_ZONE}" \
  --format='value(networkInterfaces[].accessConfigs[0].natIP.notnull().list())'

  4. The SSH Daemon's default configuration does not allow traffic forwarding to public addresses. To change it, modify the GatewayPorts options in the /etc/ssh/sshd_config file to Yes and restart the SSH daemon.

    gcloud beta compute --project="${PROJECT_ID}" ssh "${GCP_INSTANCE_NAME}" \
  --zone="${GCP_ZONE}" -- \
  sudo sed -i "s/#\?\s*GatewayPorts no/GatewayPorts Yes/" /etc/ssh/sshd_config

gcloud beta compute --project="${PROJECT_ID}" ssh "${GCP_INSTANCE_NAME}" \
  --zone="${GCP_ZONE}" -- \
  sudo service sshd restart

  5. To start port forwarding, run the following command:

    gcloud beta compute --project="${PROJECT_ID}" ssh "${GCP_INSTANCE_NAME}" \
  --zone="${GCP_ZONE}" -- \
  -N \
  -R 0.0.0.0:5555:localhost:5555 \
  -v

If you have finished using the virtual machine, remember to delete it.

gcloud beta compute --project="${PROJECT_ID}" instances delete "${GCP_INSTANCE_NAME}" \
  --zone="${GCP_ZONE}"

You can use the GCP service for free if you use the Free Tier.

DAG Testing

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

To set up the IDE:

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()
    dag.run()
  1. Set up AIRFLOW__CORE__EXECUTOR=DebugExecutor in the run configuration of your IDE. Make sure to also set up all environment variables required by your DAG.
  2. Run and 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.

Also, with the Airflow CLI command airflow dags test, you can execute one complete run of a DAG:

# airflow dags test [dag_id] [execution_date]
airflow dags test example_branch_operator 2018-01-01

By default /files/dags folder is mounted from your local <AIRFLOW_SOURCES>/files/dags and this is the directory used by airflow scheduler and webserver to scan dags for. You can place your dags there to test them.

The DAGs can be run in the main version of Airflow but they also work with older versions.

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.