DAG parsing and import errors in Apache Airflow - Commands & Configuration

Introduction

Airflow uses DAG files to know what tasks to run and when. If there is a mistake in the DAG file or its imports, Airflow cannot read it and will show parsing errors. These errors stop your workflows from running.

When you add a new DAG file and want to check if Airflow can read it without errors

When Airflow shows a red error icon on your DAG in the web UI

When you update Python code imported by your DAG and want to confirm it still loads

When debugging why a DAG does not appear in the Airflow UI

When fixing syntax or import mistakes in your DAG files

Commands

This command lists all DAGs that Airflow can successfully parse and load. It helps verify if your DAG file is recognized.

Terminal

airflow dags list

Expected Output

example_dag

This triggers a run of the DAG named example_dag. If the DAG has parsing errors, this command will fail or the run will not start.

Terminal

airflow dags trigger example_dag

Expected Output

Created <DagRun example_dag @ 2024-06-01T12:00:00+00:00: manual__2024-06-01T12:00:00+00:00, externally triggered: True>

This runs the DAG tasks for a specific date range. It helps test if the DAG can run without import or parsing errors.

Terminal

airflow dags backfill example_dag -s 2024-06-01 -e 2024-06-01

Expected Output

[2024-06-01 12:00:00,000] {taskinstance.py:1234} INFO - Starting attempt 1 of 1

[2024-06-01 12:00:01,000] {taskinstance.py:1234} INFO - Task succeeded

→

-s - Start date for backfill

→

-e - End date for backfill

This command shows the structure of the DAG. If there are parsing errors, it will fail and show error messages.

Terminal

airflow dags show example_dag

Expected Output

DAG: example_dag

Tasks:

  - start

  - process

  - end

Key Concept

If Airflow cannot read your DAG file or its imports, it will show parsing errors and the DAG will not appear or run.

Common Mistakes

Having syntax errors in the DAG Python file

Airflow cannot parse the file and will ignore the DAG, showing errors in logs

Check your Python code for syntax errors before placing it in the DAGs folder

Importing modules that are not installed or have errors

Airflow fails to import the module and cannot load the DAG

Ensure all imported modules are installed and error-free

Using relative imports incorrectly inside DAG files

Python import fails causing Airflow to fail parsing the DAG

Use absolute imports or adjust PYTHONPATH so imports work correctly

Summary

Use 'airflow dags list' to check which DAGs Airflow can parse.

Trigger or backfill DAGs to test if they run without import or parsing errors.

Fix syntax and import errors in DAG files to ensure Airflow loads them correctly.