Apache Airflow's Task Lifecycle and Architecture

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Hi, I am coder2j.

In this tutorial, I will guide you through the task life cycle and basic architecture of Apache Airflow.

By the end of this, you’ll have a solid understanding of how tasks progress from initiation to completion and how the core components of Airflow collaborate.

If you are a video person, check out the YouTube video.

Let’s dive right in!

Task Life Cycle #

Let’s start by exploring the life cycle of an Apache Airflow task. Tasks undergo various stages, each denoting a specific status.

Airflow Task can have 11 different status

There are a total of 11 stages, visualized by distinct colors in the Airflow UI (graph and tree views).

No Status to Queued #

Airflow Task from No Status to Queued

  • The task begins with no status, indicating the scheduler has created an empty task instance.
  • If fortunate, the task moves to the queued stage as the executor puts it in the task queue.

Running to Success (or Failed/Shut Down) #

Airflow Task Running to Success or Failure

  • The worker executes the task, changing its status to “running.”
  • Depending on the outcome, the task may transition to “success,” “failed,” or “shutdown.”

Retry and Reschedule #

Airflow Task Retry and Reschedule

  • In case of failure or shutdown, and if the maximum retry is not exceeded, the task may go to the “up for retry” stage.
  • Additionally, tasks in the running stage might be directed to the “up for reschedule” stage for periodic reruns.

Happy Workflow Execution Process #

Airflow Happy Workflow Execution Process In summary, a smooth workflow involves starting with no status, scheduling the task, queuing it for execution, flawless execution, and concluding with a successful task process.

Basic Architecture of Apache Airflow #

Airflow Basic Architecture Graph

Now that we understand the task life cycle, let’s explore the fundamental architecture of Apache Airflow. The following are the main components:

  1. Data Engineer:

    • Responsible for building and monitoring ETL processes.
    • Configures Airflow settings, such as the executor type and database choice.
    • Manages DAGs through the Airflow UI.
  2. Web Server, Scheduler, and Worker:

    • The web server supports the Airflow UI, visible to data engineers.
    • The scheduler manages DAG visibility and influences task status.
    • Workers execute tasks picked from the queue.

Airflow Architecture Data Engineer Component

  1. Database and Executor:
    • Connected components for persisting DAG updates and information retrieval.
    • Various database engines like MySQL or PostgreSQL can be chosen.

Airflow Database and Executor

And there you have it - a comprehensive guide to Apache Airflow’s task life cycle and basic architecture. I hope you’ve enjoyed this tutorial!

Now, it’s your turn. Do I miss anything for you to understand the Airflow task life cycle and architecture?

Let me know if you face any issues or any suggestions in the comment below.

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