How to Fix DAG Not Showing in UI in Airflow

If your DAG is not showing in the Airflow UI, first check that the DAG file is placed in the correct dags_folder and has no syntax errors. Also, ensure the DAG is not paused and the start_date is set correctly so Airflow can schedule it.

Why This Happens

This issue usually happens because Airflow cannot parse the DAG file due to syntax errors, the DAG file is not in the right folder, or the DAG's start_date is set in the future. Another common cause is that the DAG is paused or has schedule_interval=None without manual triggering.

python

from airflow import DAG
from airflow.operators.dummy import DummyOperator
from datetime import datetime

# Broken DAG with syntax error (missing colon)
default_args = {
    'start_date': datetime(2024, 1, 1)
}

dag = DAG('example_dag', default_args=default_args, schedule_interval='@daily')

start = DummyOperator(task_id='start', dag=dag)

end = DummyOperator(task_id='end', dag=dag)

start >> end  # Missing colon below causes syntax error

if __name__ == '__main__':
    print('Run DAG')

Output

SyntaxError: invalid syntax (<dag_file.py>, line 14)

The Fix

Fix the syntax errors in your DAG file and make sure it is saved in the correct dags_folder. Set the start_date to a past date and confirm the DAG is unpaused in the UI. Also, verify the DAG file loads without errors by checking Airflow logs.

python

from airflow import DAG
from airflow.operators.dummy import DummyOperator
from datetime import datetime

default_args = {
    'start_date': datetime(2024, 1, 1)
}

dag = DAG('example_dag', default_args=default_args, schedule_interval='@daily')

start = DummyOperator(task_id='start', dag=dag)
end = DummyOperator(task_id='end', dag=dag)

start >> end

if __name__ == '__main__':
    print('Run DAG')

Output

Run DAG

Prevention

Always validate your DAG files with airflow dags list or airflow dags test commands before deploying. Keep your start_date in the past and avoid setting it dynamically to future dates. Use version control and CI pipelines to catch syntax errors early. Regularly check Airflow scheduler and webserver logs for parsing errors.

Related Errors

• DAG not found error: Happens if DAG ID is misspelled or DAG file is missing.
• Scheduler not running: DAGs won't appear if the scheduler service is down.
• Import errors: External library issues can prevent DAG parsing.

Key Takeaways

Place DAG files in the correct dags_folder and fix all syntax errors.

Set start_date to a past date so Airflow can schedule the DAG.

Ensure the DAG is unpaused in the Airflow UI to make it visible.

Use airflow CLI commands to validate DAG files before deployment.

Check scheduler and webserver logs for parsing or import errors.