- Airflow Test Infrastructure
- Airflow Unit Tests
- Helm Unit Tests
- Airflow Integration Tests
- Airflow test types
- Running full Airflow test suite in parallel
- Running Tests with provider packages
- Running Tests with Kubernetes
- Airflow System Tests
- Local and Remote Debugging in IDE
- DAG Testing
- Tracking SQL statements
- 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.
All tests for Apache Airflow are run using pytest .
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 usepytest
by default. - For new tests, use standard "asserts" of Python and
pytest
decorators/context managers for testing rather thanunittest
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.
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:
and run unit tests as follows:
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.
To run unit tests from the Visual Studio Code:
- Using the
Extensions
view install Python extension, reload if required
- Using the
Testing
view click onConfigure Python Tests
and selectpytest
framework
- Open
/.vscode/settings.json
and add"python.testing.pytestArgs": ["tests"]
to enable tests discovery
- Now you are able to run and debug tests from both the
Testing
view and test files
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
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.
breeze 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 tests --db-reset
You can also specify individual tests or a group of tests:
breeze tests --db-reset tests/core/test_core.py::TestCore
You can also limit the tests to execute to specific group of tests
breeze tests --test-type Core
In case of Providers tests, you can run tests for all providers
breeze tests --test-type Providers
You can also limit the set of providers you would like to run tests of
breeze tests --test-type "Providers[airbyte,http]"
You can also write tests in "limited progress" mode (useful in the future to run CI). In this mode each test just prints "percentage" summary of the run as single line and only dumps full output of the test after it completes.
breeze tests --test-type Core --limit-progress-output
You can also run tests for a specific test type. For the stability and performance point of view, we separated tests into different test types to be run separately.
You can select the test type by adding --test-type TEST_TYPE
before the test command. There are two
kinds of test types:
Per-directories types are added to select subset of the tests based on sub-directories in
tests
folder. Example test types there - Core, Providers, CLI. The only action that happens when you choose the right test folders are pre-selected. It is only useful for those types of tests to choose the test type when you do not specify test to run.Runs all core tests:
./breeze-legacy --test-type Core --db-reset tests
Runs all provider tests:
./breeze-legacy --test-type Providers --db-reset tests
Special kinds of tests - Integration, Quarantined, Postgres, MySQL, which are marked with pytest marks and for those you need to select the type using test-type switch. If you want to run such tests using breeze, you need to pass appropriate
--test-type
otherwise the test will be skipped. Similarly to the per-directory tests if you do not specify the test or tests to run, all tests of a given type are runRun quarantined test_task_command.py test:
./breeze-legacy --test-type Quarantined tests tests/cli/commands/test_task_command.py --db-reset
Run all Quarantined tests:
./breeze-legacy --test-type Quarantined tests --db-reset
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 tests/charts
.
class TestBaseChartTest(unittest.TestCase):
...
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.helm_template_generator import render_chart, render_k8s_object
git_sync_basic = """
dags:
gitSync:
enabled: true
"""
class TestGitSyncScheduler(unittest.TestCase):
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 run tests using breeze run the following command
./breeze-legacy --test-type Helm tests
Some of the tests in Airflow are integration tests. These tests require airflow
Docker
image and extra images with integrations (such as redis
, mongodb
, etc.).
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 .github/workflows/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 using the --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 restart it
via restart
command.
The following integrations are available:
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 |
pinot | Integration required for Apache Pinot hooks |
rabbitmq | Integration required for Celery executor tests |
redis | 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 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.
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()
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
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/
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
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.
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.
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:
- in order to selectively run only subset of the test types for some PRs
- 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.
If you run ./scripts/ci/testing/ci_run_airflow_testing.sh
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 (~32 GB), the Integration
test type
is always run sequentially - after all tests are completed (docker cleanup is performed in-between).
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)
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:
- Prepare provider packages
breeze 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.
- Prepare airflow packages
breeze prepare-airflow-package
This prepares airflow .whl package in the dist folder.
- Enter breeze installing both airflow and providers from the packages
This installs airflow and enters
./breeze-legacy --use-airflow-version wheel --use-packages-from-dist --skip-mounting-local-sources
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.
Configuration for the cluster is kept in ./build/.kube/config
file in your Airflow source repository, and
our scripts set the KUBECONFIG
variable to it. If you want to interact with the Kind cluster created
you can do it from outside of the scripts by exporting this variable and point it to this file.
For your testing, you manage Kind cluster with kind-cluster
breeze command:
./breeze-legacy kind-cluster [ start | stop | recreate | status | deploy | test | shell | k9s ]
The command allows you to start/stop/recreate/status Kind Kubernetes cluster, deploy Airflow via Helm chart as well as interact with the cluster (via test and shell commands).
Setting up the Kind Kubernetes cluster takes some time, so once you started it, the cluster continues running
until it is stopped with the kind-cluster stop
command or until kind-cluster recreate
command is used (it will stop and recreate the cluster image).
The cluster name follows the pattern airflow-python-X.Y-vA.B.C
where X.Y 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.
Deploying Airflow to the Kubernetes cluster created is also done via kind-cluster deploy
breeze command:
./breeze-legacy kind-cluster deploy
The deploy command performs those steps:
- It rebuilds the latest
apache/airflow:main-pythonX.Y
production images using the latest sources using local caching. It also adds example DAGs to the image, so that they do not have to be mounted inside. - Loads the image to the Kind Cluster using the
kind load
command. - Starts airflow in the cluster using the official helm chart (in
airflow
namespace) - Forwards Local 8080 port to the webserver running in the cluster
- Applies the volumes.yaml to get the volumes deployed to
default
namespace - this is where KubernetesExecutor starts its pods.
You can also specify a different executor by providing the --executor
optional argument:
./breeze-legacy kind-cluster deploy --executor CeleryExecutor
Note that when you specify the --executor
option, it becomes the default. Therefore, every other operations
on ./breeze-legacy kind-cluster
will default to using this executor. To change that, use the --executor
option on the
subsequent commands too.
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 shell:
export EXECUTOR="KubernetesExecutor" ## can be also CeleryExecutor or CeleryKubernetesExecutor
./scripts/ci/kubernetes/ci_run_kubernetes_tests.sh - runs all kubernetes tests
./scripts/ci/kubernetes/ci_run_kubernetes_tests.sh TEST [TEST ...] - runs selected kubernetes tests (from kubernetes_tests folder)
Running Kubernetes tests via breeze:
./breeze-legacy kind-cluster test
./breeze-legacy kind-cluster test -- TEST TEST [TEST ...]
Optionally add --executor
:
./breeze-legacy kind-cluster test --executor CeleryExecutor
./breeze-legacy kind-cluster test -- TEST TEST [TEST ...] --executor CeleryExecutor
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 binaries are available in ./.build/kubernetes-bin/KUBERNETES_VERSION
path.
The virtualenv is available in ./.build/.kubernetes_venv/KIND_CLUSTER_NAME``_host_python_``HOST_PYTHON_VERSION
Where KIND_CLUSTER_NAME
is the name of the cluster and HOST_PYTHON_VERSION
is the version of python
in the host.
You can enter the shell via those scripts
export EXECUTOR="KubernetesExecutor" ## can be also CeleryExecutor or CeleryKubernetesExecutor
./scripts/ci/kubernetes/ci_run_kubernetes_tests.sh [-i|--interactive] - Activates virtual environment ready to run tests and drops you in
./scripts/ci/kubernetes/ci_run_kubernetes_tests.sh [--help] - Prints this help message
./breeze-legacy kind-cluster shell
Optionally add --executor
:
./breeze-legacy kind-cluster shell --executor CeleryExecutor
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:
You can enter the k9s tool via breeze (after you deployed Airflow):
./breeze-legacy kind-cluster k9s
You can exit k9s by pressing Ctrl-C.
The typical session for tests with Kubernetes looks like follows:
- Start the Kind cluster:
./breeze-legacy kind-cluster start
Starts Kind Kubernetes cluster
Use CI image.
Branch name: main
Docker image: apache/airflow:main-python3.7-ci
Airflow source version: 2.0.0.dev0
Python version: 3.7
Backend: postgres 10
No kind clusters found.
Creating cluster
Creating cluster "airflow-python-3.7-v1.17.0" ...
✓ Ensuring node image (kindest/node:v1.17.0) 🖼
✓ Preparing nodes 📦 📦
✓ Writing configuration 📜
✓ Starting control-plane 🕹️
✓ Installing CNI 🔌
Could not read storage manifest, falling back on old k8s.io/host-path default ...
✓ Installing StorageClass 💾
✓ Joining worker nodes 🚜
Set kubectl context to "kind-airflow-python-3.7-v1.17.0"
You can now use your cluster with:
kubectl cluster-info --context kind-airflow-python-3.7-v1.17.0
Have a question, bug, or feature request? Let us know! https://kind.sigs.k8s.io/#community 🙂
Created cluster airflow-python-3.7-v1.17.0
- Check the status of the cluster
./breeze-legacy kind-cluster status
Checks status of Kind Kubernetes cluster
Use CI image.
Branch name: main
Docker image: apache/airflow:main-python3.7-ci
Airflow source version: 2.0.0.dev0
Python version: 3.7
Backend: postgres 10
airflow-python-3.7-v1.17.0-control-plane
airflow-python-3.7-v1.17.0-worker
- Deploy Airflow to the cluster
./breeze-legacy kind-cluster deploy
- 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.
4a) You can run all the tests
./breeze-legacy kind-cluster test
4b) You can enter an interactive shell to run tests one-by-one
This prepares and enters the virtualenv in .build/.kubernetes_venv_<YOUR_CURRENT_PYTHON_VERSION>
folder:
./breeze-legacy kind-cluster shell
Once you enter the environment, you receive this information:
Activating the virtual environment for kubernetes testing
You can run kubernetes testing via 'pytest kubernetes_tests/....'
You can add -s to see the output of your tests on screen
The webserver is available at http://localhost:8080/
User/password: admin/admin
You are entering the virtualenv now. Type exit to exit back to the original shell
In a separate terminal you can open the k9s CLI:
./breeze-legacy kind-cluster k9s
Use it to observe what's going on in your cluster.
- 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-legacy kind-cluster 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:
- Add the virtualenv as interpreter for the project:
The virtualenv is created in your "Airflow" source directory in the
.build/.kubernetes_venv_<YOUR_CURRENT_PYTHON_VERSION>
folder and you
have to find python
binary and choose it when selecting interpreter.
- Choose pytest as test runner:
- Run/Debug tests using standard "Run/Debug" feature of IntelliJ
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-legacy kind-cluster test":
echo ${KUBECONFIG}
/home/jarek/code/airflow/.build/.kube/config
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.
pytest kubernetes_tests/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.
Sometimes there are side effects from running tests. You can run redeploy_airflow.sh
without
recreating the whole cluster. This will delete the whole namespace, including the database data
and start a new Airflow deployment in the cluster.
./scripts/ci/redeploy_airflow.sh
If needed you can also delete the cluster manually:
kind get clusters
kind delete clusters <NAME_OF_THE_CLUSTER>
Kind has also useful commands to inspect your running cluster:
kind --help
However, when you change Kubernetes executor implementation, you need to redeploy Airflow to the cluster.
./breeze-legacy kind-cluster deploy
- Stop KinD cluster when you are done
./breeze-legacy kind-cluster stop
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.
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.
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
foraws
- Amazon Web Services client - credentials stored in
${HOME}/.azure
foraz
- Microsoft Azure client - credentials stored in
${HOME}/.config
forgcloud
- Google Cloud client (among others) - credentials stored in
${HOME}/.docker
fordocker
client - credentials stored in
${HOME}/.snowsql
forsnowsql
- SnowSQL (Snowflake CLI client)
- credentials stored in
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 theproviders
package (for example, the system tests fromtests.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 useself.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 thetests.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
.
To run system tests with the older Airflow version, you need to prepare provider packages. This
can be done by running ./breeze-legacy prepare-provider-packages <PACKAGES TO BUILD>
. For
example, the below command will build google, postgres and mysql wheel packages:
breeze prepare-provider-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.
Here is the typical session that you need to do to run system tests:
- Enter breeze
breeze stop
breeze --python 3.7 --db-reset --forward-credentials
This will:
- stop the whole environment (i.e. recreates metadata database from the scratch)
- run Breeze with: * python 3.7 version * resetting the Airflow database * forward your local credentials to Breeze
- Run the tests:
pytest -o faulthandler_timeout=2400 \
--system=google tests/providers/google/cloud/operators/test_compute_system.py
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 !!!!!!!!!!!!!!!!!!!!!!!!!!!!
- Enter breeze
breeze stop
breeze --python 3.7 --db-reset --forward-credentials
- Run create action in helper (to create slowly created resources):
python tests/providers/google/cloud/operators/test_cloud_sql_system_helper.py --action create
- Run the tests:
pytest -o faulthandler_timeout=2400 \
--system=google tests/providers/google/cloud/operators/test_compute_system.py
- Run delete action in helper:
python tests/providers/google/cloud/operators/test_cloud_sql_system_helper.py --action delete
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:
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:
Below are the steps you need to take to set up your virtual machine in the Google Cloud.
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"
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 "${GCP_NETWORK_NAME}" \ --allow tcp:5555 \ --source-ranges 0.0.0.0/0
If you have a network, you can create a virtual machine. To save costs, you can create a Preemptible virtual machine <https://cloud.google.com/preemptible-vms> that is automatically deleted for up to 24 hours.
gcloud beta compute --project="${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())'
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 toYes
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
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.
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()
- 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. - 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.
To run the tests for Airflow 1.10.* series, you need to run Breeze with
--use-airflow-pypi-version=<VERSION>
to re-install a different version of Airflow.
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 --use-airflow-pypi-version=1.10.9 restart
.
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.