Mastering Airflow’s “tasks” command

Apache Airflow

Apache Airflow is an open-source platform designed to programmatically author, schedule, and monitor workflows. While DAGs (Directed Acyclic Graphs) are the backbone of Airflow, each DAG consists of tasks that represent the individual operations within a workflow. Airflow’s tasks command provides users with a suite of sub-commands to interact with, test, and manage these tasks. In this article, we’ll deep dive into the tasks command, covering its functionalities with practical examples.

Overview of the tasks Command:

The tasks command is a versatile tool for task-level operations within a DAG. From testing to checking the status, this command offers a comprehensive toolset for task management.


The general format of the tasks command is:

<SUBCOMMAND>: Represents the operation you want to perform on the task.
<DAG_ID>: The identifier of your DAG.
<TASK_ID>: The identifier of the task within the DAG.
<EXECUTION_DATE>: The execution date for the task. It should be provided in the ‘YYYY-MM-DD’ format.

Available Sub-Commands:


This sub-command lets you test a specific task in a DAG for a particular execution date without creating any records in the metadata database.


airflow tasks test freshers_in_sample_dag start_task 2023-01-01
This triggers a task instance to run. Unlike test, the run sub-command will create a record in the database.


airflow tasks run freshers_in_sample_dag start_task 2023-01-01


It clears the status of a task instance (or instances) for a specific execution date. Useful for re-running tasks.


airflow tasks clear freshers_in_sample_dag start_task -e 2023-01-01


This checks the current state of a task instance.


airflow tasks state freshers_in_sample_dag start_task 2023-01-01


Lists all tasks in a DAG.


airflow tasks list freshers_in_sample_dag


Checks why a task could not run for a specific execution date due to unmet dependencies.


airflow tasks failed-deps freshers_in_sample_dag start_task 2023-01-01

Sample DAG:

To provide context, let’s use the following simple DAG:

from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.dummy_operator import DummyOperator
default_args = {
    'owner': 'airflow',
    'start_date': datetime(2023, 1, 1),
    'retries': 1,
    'retry_delay': timedelta(minutes=5)
dag = DAG('freshers_in_sample_dag',
          description='A basic tutorial DAG',
start = DummyOperator(task_id='start_task', dag=dag)
end = DummyOperator(task_id='end_task', dag=dag)
start >> end

Why Use the tasks Command?

Focused Testing: Enables you to test individual tasks rather than the entire DAG.
Flexibility: Offers a comprehensive suite of operations for task management, suitable for various scenarios.
Troubleshooting: Commands like failed-deps help in identifying issues preventing a task from running.
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