#Trigger airflow dag how to#
#Trigger airflow dag manual#
#Trigger airflow dag code#

If you are an Astro customer, you can find further information on how to set up CI/CD to your Astro deployment in CI/CD. For example, to run airflow dags test on the DAG my_dag for the execution date of run: With the Astro CLI, you can run all Airflow CLI commands using astro dev run.

airflow tasks test: This command tests one specific task instance without checking for dependencies or recording the outcome in the metadata database.

#Trigger airflow dag manual#

This command is useful for testing full DAGs by creating manual DAG runs from the command line. airflow dags test: Given a DAG ID and execution date, this command writes the results of a single DAG run to the metadata database.The Airflow CLI offers two commands related to local testing: For more information on the Astro CLI's testing capabilities, see Test and troubleshoot locally. This folder contains the test_dag_integrity.py script defining several examples of using pytest with Airflow. astro dev pytest: will run all pytest test suites in the test directory of your current Airflow project.Įvery new Astro project will be initialized with a test/dags folder in your Astro project directory.astro dev parse: will quickly parse your DAGs to find any Python syntax or DAG import errors.

Airflow does not need to be running to use these commands. The Astro CLI includes two commands to run DAG validation tests. If you are new to testing Airflow DAGs, you can quickly get started by using Astro CLI commands. This section gives an overview of the most common implementation methods. import_errors attribute of the current DagBag.Īirflow offers different ways to run DAG validation tests using any Python test runner. In the following test, get_import_errors checks the. Checking for import errors through a validation test is faster than starting your Airflow environment and checking for errors in the Airflow UI. The most common DAG validation test is to check for import errors.

#Trigger airflow dag code#

This section covers the most common types of DAG validation tests with full code examples. Additional tests can check things like custom logic, ensuring that catchup is set to False for every DAG in your Airflow instance, or making sure only tags from a defined list are used in the DAGs.ĭAG validation tests apply to all DAGs in your Airflow environment, so you only need to create one test suite. Enable power users to test DAGs from the CLI.Īt a minimum, you should run DAG validation tests to check for import errors.Test DAGs automatically in a CI/CD pipeline.Ensure that custom DAG requirements are systematically checked and fulfilled.Develop DAGs without access to a local Airflow environment.See Get started with Airflow.ĭAG validation tests ensure that your DAGs fulfill a list of criteria. See Continuous Integration with Python: An Introduction. This guide mostly uses pytest, but you can use others including nose2 and unittest. See Getting Started with Testing in Python. To get the most out of this guide, you should have an understanding of: In this guide, you'll learn about various types of DAG validation testing, unit testing, and where to find further information on data quality checks. Effectively testing DAGs requires an understanding of their structure and their relationship to other code and data in your environment.