11/19/2023 0 Comments Airflow dag unit testing![]() This too had the secondary benefit of enabling unit testing, as with the cloud. Your DAGs are running happily in production without throwing error emails. In another situation, our Airflow DAGS were set up based on the tier of. That said, the logic of the DAG should be tested in your dev / staging environment before running it in production if you want to avoid bad surprises. DAGs are not the main piece of the logic. You should embrace the power of Airflow to define DAGs with Python code and treat them as just wiring pieces you’ve tested individually together. it’ll be often very hard to do as you’ll likely need various components (databases, external systems, files) and can make your test suite slow.I would say that it’s not worth testing an end to end DAG logic because: Self.assertIn(self.AIRFLOW_ALERT_EMAIL, email_list, msg) Verify that Airflow will be able to import all DAGs in the repository. Generic tests that all DAGs in the repository should be able to pass.ĪIRFLOW_ALERT_EMAIL = ' ' It relies heavily on the code provided by WePay in this blog post. Here is an example test file to test this. Airflow email alerts are properly defined on all DAGs.a single file defining multiple DAGs loads fast enough.It’ll show in your CI environment if some DAGs expect a specific state (a CSV file to be somewhere, a network connection to be opened) to be able to be loaded or if you need to define environment / Airflow variables for example An example unit test for a DAG Code sample Python To authenticate to Cloud Composer, set up Application Default Credentials. each DAG can be loaded by the Airflow scheduler without any failure.Smoke testįinally, the last test I would recommend writing is a smoke test that will target all DAGs. You’ll just need to perform a single function call from your DAG’s PythonOperator after. With this, you’ll be able to keep your Python logic away from Airflow internals and it’ll be easier to test it. then disconnect the wires and test them for comparison. If you’re using a non trivial logic from a PythonOperator, I would recommend about extracting this logic into a Python module named after the DAG ID. 70 A Outdoor unit model SUZ-KA24NAHZ SUZ-KA30NAHZ Phase Single (Lossnay) High power Comfort. creating a Python class that will act as a factory to create the underlying Airflow operator with the common arguments you’re using.creating a custom Airflow operator thanks to the plugin mechanism. ![]() If you’re using several times the same operator in different DAGs with a similar construction method, I would recommend about either: Cloud Build runs unit tests to check that your DAG is valid. You open a pull request against the main branch of your repository. In this quick blog post, I’ll share what’s it’s worth testing according to me. The CI/CD pipeline that to test, synchronize, and deploys DAGs has the following steps: You make a change to a DAG and push that change to a development branch in your repository. Obligation de réutiliser des données de transportĭuring my job at Drivy as a Data Engineer, I had the chance to write close to 100 main Airflow DAGs.), I don't have public resources about this part, but the implementation is not very complicated. ) which has the same Metastore (Airflow db) configuration, then use DagBag class to find the dags and create runs, finally check the state of the tasks each x seconds and check if they did their job (the result is written to an external system, check the xcom. So you can any testing frameworks to implement Airflow tests (Robot, PyTest, Unittest, DocTest, Nose2, Testify.).įor testing, you can check the dags files parsing performance, and if there is a problem in this parsing (check this answer), you can test the operator by preparing the task context and calling the method execute ( here is some examples), for integration tests you can use mocks (check these tests used to test Airflow official operators), and if you want to implement some functional tests, you can run Airflow server, and create a separate process (local, docker, k8s pod. Airflow is a python package similar to other packages in concept, it contains a group of python classes which interact with each other and with other classes to run the dag tasks. ![]()
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