Skip to content

Latest commit

 

History

History
983 lines (656 loc) · 38.9 KB

TESTING.rst

File metadata and controls

983 lines (656 loc) · 38.9 KB
Raw

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. Currently, these tests are not marked as integration tests but soon they will be separated by pytest annotations.
  • System tests are automatic tests that use external systems like Google Cloud Platform. These tests are intended for an end-to-end DAG execution. The tests can be executed on both the current version of Apache Airflow and any of the 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.

NOTE: We plan to convert all unit tests to standard "asserts" semi-automatically but this will be done later in Airflow 2.0 development phase. That will include setUp/tearDown/context managers and decorators.

Running Unit Tests from IDE

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

Configuring test runner

and run unit tests as follows:

Running unit tests

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

Running Unit Tests

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

Custom pytest plugin run airflow db init and airflow db reset the first time you launch them. So, you can count on the database being initialized. Currently, when you run tests not supported in the local virtualenv, 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 -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/tests_core.py -k "TestCore and not bash".

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

pytest tests/tests_core.py -k "test_check_operators"

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

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

To run the whole test class, enter:

pytest tests/test_core.py::TestCore

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

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

Running Tests for a Specified Target Using Breeze from the Host

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

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

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

./breeze test-target .

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

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

Airflow Integration Tests

Some of the tests in Airflow are integration tests. These tests require not only airflow Docker image but also extra images with integrations (such as redis, mongodb, etc.).

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.yml 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 by using the --integration all switch that enables all integrations.

NOTE: Every integration requires a separate container with the corresponding integration image. They 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 restart it via restart 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
openldap Integration required for OpenLDAP hooks
rabbitmq Integration required for Celery executor tests
redis Integration required for Celery executor tests

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 integrations, enter:

./breeze --integration all

In the CI environment, integrations can be enabled by specifying the ENABLED_INTEGRATIONS variable storing a space-separated list of integrations to start. Thanks to that, we can run integration and integration-less tests separately in different jobs, which is desired from the memory usage point of view.

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 redis integration test:

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

    self.assertTrue(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

To run integration tests for mongo and rabbitmq:

pytest --integration mongo --integration rabbitmq

Note that collecting all tests takes some time. So, if you know where your tests are located, you can speed up the test collection significantly by providing the folder where the tests are located.

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

pytest --integration cassandra tests/providers/apache/

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.

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 Kubernetes

Starting Kubernetes Cluster when Starting Breeze

To run Kubernetes in Breeze, you can start Breeze with the --kind-cluster-start switch. This automatically creates a Kind Kubernetes cluster in the same docker engine that is used to run Breeze. Setting up the Kubernetes cluster takes some time so the cluster continues running until it is stopped with the --kind-cluster-stop switch or until the --kind-cluster-recreate switch is used rather than --kind-cluster-start. Starting Breeze with the Kind Cluster automatically sets runtime to kubernetes (see below).

The cluster name follows the pattern airflow-python-X.Y.Z-vA.B.C where X.Y.Z is a Python version and A.B.C is a Kubernetes version. This way you can have multiple clusters set up and running at the same time for different Python versions and different Kubernetes versions.

The Control Plane is available from inside the Docker image via <CLUSTER_NAME>-control-plane:6443 host:port, the worker of the Kind Cluster is available at <CLUSTER_NAME>-worker and the webserver port for the worker is 30809.

After the Kubernetes Cluster is started, you need to deploy Airflow to the cluster:

  1. Build the image.
  2. Load it to the Kubernetes cluster.
  3. Deploy the Airflow application.

It can be done with a single script: ./scripts/ci/in_container/deploy_airflow_to_kubernetes.sh.

You can, however, work separately on the image in Kubernetes and deploying the Airflow app in the cluster.

Building and Loading Airflow Images to Kubernetes Cluster

Use the script ./scripts/ci/in_container/kubernetes/docker/rebuild_airflow_image.sh that does the following:

  1. Rebuilds the latest apache/airflow:master-pythonX.Y-ci images using the latest sources.
  2. Builds a new Kubernetes image based on the apache/airflow:master-pythonX.Y-ci using necessary scripts added to run in Kubernetes. The image is tagged as apache/airflow:master-pythonX.Y-ci-kubernetes.
  3. Loads the image to the Kind Cluster using the kind load command.

Deploying the Airflow Application in the Kubernetes Cluster

Use the script ./scripts/ci/in_container/kubernetes/app/deploy_app.sh that does the following:

  1. Prepares Kubernetes resources by processing a template from the template directory and replacing variables with the right images and locations: - configmaps.yaml - airflow.yaml
  2. Uses the existing resources without replacing any variables inside: - secrets.yaml - postgres.yaml - volumes.yaml
  3. Applies all the resources to the Kind Cluster.
  4. Waits for all the applications to be ready and reachable.

After the deployment is finished, you can run Kubernetes tests immediately in the same way as other tests. The Kubernetes tests are available in the tests/runtime/kubernetes folder.

You can run all the integration tests for Kubernetes with pytest tests/runtime/kubernetes.

Running Runtime-Specific Tests

Tests using a specific runtime are marked with a custom pytest marker pytest.mark.runtime. The marker has a single parameter - the name of a runtime. At the moment the only supported runtime is kubernetes. This runtime is set when you run Breeze with one of the --kind-cluster-* flags. Runtime-specific tests run only when the selectd runtime is started.

@pytest.mark.runtime("kubernetes")
class TestKubernetesExecutor(unittest.TestCase):

You can use the custom --runtime switch in pytest to only run tests specific for that backend.

To run only kubernetes-runtime backend tests, enter:

pytest --runtime kubernetes

NOTE: For convenience and faster search, all runtime tests are stored in the tests.runtime package. In this case, you can speed up the collection of tests by running:

pytest --runtime kubernetes tests/runtime

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}")

# install any packages from dist folder if they are available
if [[ ${RUN_AIRFLOW_1_10:=} == "true" ]]; then
    pip install /dist/apache_airflow_backport_providers_{google,postgres,mysql}*.whl || true
fi

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:
  • all credentials stored in ${HOME}/.config (for example, GCP credentials)
  • credentials stored in ${HOME}/.gsutil for gsutil tool from GCS
  • credentials stored in ${HOME}/.aws, ${HOME}/.boto, and ${HOME}/.s3 (for AWS authentication)
  • credentials stored in ${HOME}/.docker for docker
  • credentials stored in ${HOME}/.kube for kubectl

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-runnin 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 and airflow.providers.google.cloud.example_dags.example_compute_igm.py.

Preparing backport packages for System Tests for Airflow 1.10.* series

To run system tests with old Airflow version you need to prepare backport packages. This can be done by running ./breeze prepare-backport-packages -- <PACKAGES TO BUILD>. For example the below command will build google postgres and mysql packages:

./breeze prepare-backport-packages -- google postgres mysql

Those packages will be prepared in ./dist folder. This folder is mapped to /dist folder when you enter Breeze, so it is easy to automate installing those packages for testing.

Installing backported for Airflow 1.10.* series

The tests can be executed against the master version of Airflow but they also work with older versions. This is especially useful to test back-ported operators from Airflow 2.0 to 1.10.* versions.

To run the tests for Airflow 1.10.* series, you need to run Breeze with --install-airflow-version=<VERSION> to install a different version of Airflow. If current is specified (default), then the current version of Airflow is used. Otherwise, the released version of Airflow is installed.

The -install-airflow-version=<VERSION> command make sure that the current (from sources) version of Airflow is removed and the released version of Airflow from Pypi is installed. Note that tests sources are not removed and they can be used to run tests (unit tests and system tests) against the freshly installed version.

You should automate installing of the backport packages in your own ./files/airflow-breeze-config/variables.env file. You should make it depend on RUN_AIRFLOW_1_10 variable value equals to "true" so that the installation of backport packages is only performed when you install airflow 1.10.*. The backport packages are available in /dist directory if they were prepared as described in the previous chapter.

Typically the command in you variables.env file will be similar to:

# install any packages from dist folder if they are available
if [[ ${RUN_AIRFLOW_1_10:=} == "true" ]]; then
    pip install /dist/apache_airflow_backport_providers_{google,postgres,mysql}*.whl || true
fi

The command above will automatically install backported google, postgres, and mysql packages if they were prepared before entering the breeze.

Running system tests for backported packages in Airflow 1.10.* series

Once you installed 1.10.* Airflow version with --install-airflow-version and prepared and installed the required packages via variables.env it should be as easy as running pytest --system=<SYSTEM_NAME> TEST_NAME. Note that we have default timeout for running system tests set to 8 minutes and some system tests might take much longer to run and you might want to add -o faulthandler_timeout=2400 (2400s = 40 minutes for example) to your pytest command.

The typical system test session

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

  1. Prepare backport packages
./breeze prepare-backport-packages -- google postgres mysql
  1. Enter breeze with installing Airflow 1.10.*, forwarding credentials and installing backported packages (you need an appropriate line in ./files/airflow-breeze-config/variables.env)
./breeze --install-airflow-version 1.10.9 --python 3.6 --db-reset --forward-credentials restart

This will:

  • install Airflow 1.10.9
  • restarts the whole environment (i.e. recreates metadata database from the scratch)
  • run Breeze with python 3.6 version
  • reset 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 !!!!!!!!!!!!!!!!!!!!!!!!!!!!

Note that in case you have to update your backported operators or system tests (they are part of the backport packageS) you need to rebuild the packages outside of breeze and pip remove/pip install those packages to get them installed. This is not needed if you run system tests with current airflow version, so it is better to iterate with the system tests with the current version and fix all problems there and only afterwards run the tests with Airflow 1.10.*

The typical session then looks as follows:

  1. Prepare backport packages
./breeze prepare-backport-packages -- google postgres mysql
  1. Enter breeze with installing Airflow 1.10.*, forwarding credentials and installing backported packages (you need an appropriate line in ./files/airflow-breeze-config/variables.env)
./breeze --install-airflow-version 1.10.9 --python 3.6 --db-reset --forward-credentials restart
  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. In case you are running backport packages tests you need to rebuild and reinstall a package every time you change the operators/hooks or example_dags. The example below shows reinstallation of the google package:

In the host:

./breeze prepare-backport-packages -- google

In the container:

pip uninstall apache-airflow-backport-providers-google
pip install /dist/apache_airflow_backport_providers_google-*.whl

The points 4. and 5. can be repeated multiple times without leaving the container

  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 Platform.

  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-10-buster-v20200210 \
  --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 Deamon'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(reset_dag_runs=True)
  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 master version of Airflow but they also work with older versions.

To run the tests for Airflow 1.10.* series, you need to run Breeze with --install-airflow-version==<VERSION> to install a different version of Airflow. If current is specified (default), then the current version of Airflow is used. Otherwise, the released version of Airflow is installed.

You should also consider running it with restart command when you change the installed version. This will clean-up the database so that you start with a clean DB and not DB installed in a previous version. So typically you'd run it like breeze --install-airflow-version=1.10.9 restart.

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.

BASH Unit Testing (BATS)

We have started adding tests to cover Bash scripts we have in our codebase. The tests are placed in the tests\bats folder. They require BAT CLI to be installed if you want to run them on your host or via a Docker image.

Installing BATS CLI

You can find an installation guide as well as information on how to write the bash tests in BATS Installation.

Running BATS Tests on the Host

To run all tests:

bats -r tests/bats/

To run a single test:

bats tests/bats/your_test_file.bats

Running BATS Tests via Docker

To run all tests:

docker run -it --workdir /airflow -v $(pwd):/airflow  bats/bats:latest -r /airflow/tests/bats

To run a single test:

docker run -it --workdir /airflow -v $(pwd):/airflow  bats/bats:latest /airflow/tests/bats/your_test_file.bats

Using BATS

You can read more about using BATS CLI and writing tests in BATS Usage.