Introduction to DAGs in Airflow

What is a DAG in Airflow?

A DAG, or Directed Acyclic Graph, is the blueprint of your workflow in Airflow—a Python script that outlines a series of tasks and their dependencies. The “directed” part means tasks flow in a specific direction—like Task A before Task B—while “acyclic” ensures there are no loops, so the workflow has a clear start and end. You define a DAG using the DAG class in Python, giving it a unique dag_id, a start_date, and a schedule_interval (e.g., @daily) to tell Airflow when to run it. These scripts live in the dags folder—typically ~/airflow/dags, managed via DAG File Structure Best Practices—and Airflow’s Scheduler reads them to execute your tasks in order, as detailed in Airflow Architecture (Scheduler, Webserver, Executor).

Think of a DAG as a map: it shows Airflow the path from start to finish, with tasks as stops and dependencies as roads. For example, a DAG might extract data, process it, and load it into a database—each step a task, linked logically without circling back.

Why DAGs Matter in Airflow

DAGs are the backbone of Airflow’s workflow orchestration—without them, your tasks would lack structure and automation. They define what runs, when it runs, and how tasks depend on each other—crucial for ensuring a task like “process data” waits for “extract data” to finish (DAG Dependencies and Task Ordering). The Scheduler uses the DAG’s schedule_interval to queue tasks (DAG Scheduling (Cron, Timetables)), the Executor runs them (Airflow Executors (Sequential, Local, Celery)), and the metadata database tracks it all (Airflow Metadata Database Setup). DAGs turn your ideas into repeatable, trackable workflows, viewable in Airflow Web UI Overview).

Without DAGs, you’d be stuck running scripts manually—no automation, no retries (Task Retries and Retry Delays), no visibility. They’re what make Airflow a powerhouse for data pipelines.

How DAGs Work in Airflow

A DAG starts as a Python script you write—Airflow’s Scheduler scans the dags folder every few minutes (set via Airflow Configuration Options), reads the script, and builds a graph of tasks and dependencies. The start_date and schedule_interval determine when it runs—say, daily from January 1, 2025. When the scheduled time hits, the Scheduler queues the tasks, respecting dependencies (e.g., task1 >> task2 means task2 waits). The Executor—Sequential, Local, or Celery—picks them up and runs their code, updating the database with states like “success” or “failed.” You can watch this in the UI at localhost:8080 or trigger it manually with Triggering DAGs via UI). It’s a seamless process from code to execution, all driven by the DAG.

Anatomy of a DAG

A DAG has key parts that make it work—let’s break them down.

DAG ID

The dag_id is a unique name for your DAG—like “my_etl”—set in the DAG constructor (dag_id="my_etl"). It’s how Airflow identifies it in the UI and CLI (Airflow CLI: Overview and Usage)—keep it short, unique, and descriptive.

Start Date

The start_date—e.g., datetime(2025, 1, 1)—tells Airflow when the DAG begins. It works with schedule_interval to calculate run dates—set it in the past for backfills (Catchup and Backfill Scheduling), but use catchup=False to skip old runs.

Schedule Interval

The schedule_interval—like @daily or 0 0 * * *—defines how often the DAG runs. Airflow’s Scheduler uses it to queue tasks—e.g., @daily means midnight each day. Set it to None for manual runs—details in DAG Scheduling (Cron, Timetables).

Tasks and Dependencies

Tasks are the jobs—like BashOperator echoing a message—linked by dependencies (e.g., task1 >> task2). They’re defined inside the DAG’s with block, forming the graph—more in Defining DAGs in Python).

Creating Your First DAG

Let’s build a simple DAG to see it in action.

Step 1: Set Up Airflow

1. Install Airflow: Follow Installing Airflow (Local, Docker, Cloud)—open your terminal, type cd ~, press Enter, then python -m venv airflow_env, source airflow_env/bin/activate (Mac/Linux) or airflow_env\Scripts\activate (Windows), and pip install apache-airflow.
2. Initialize the Database: Type airflow db init and press Enter—it creates ~/airflow/airflow.db.
3. Start Services: In one terminal, activate, type airflow webserver -p 8080, and press Enter. In another, activate, type airflow scheduler, and press Enter.

Step 2: Write a Simple DAG

1. Open Your Text Editor: Use Notepad (Windows), TextEdit (Mac), or VS Code.
2. Write the DAG Code: Paste this:

Copy codefrom airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime

with DAG(
    dag_id="intro_dag",
    start_date=datetime(2025, 1, 1),
    schedule_interval="@daily",
    catchup=False,
) as dag:
    start = BashOperator(
        task_id="start",
        bash_command="echo 'Workflow starting!'",
    )
    end = BashOperator(
        task_id="end",
        bash_command="echo 'Workflow complete!'",
    )
    start >> end

3. Save the File: Save as intro_dag.py in ~/airflow/dags—e.g., /home/username/airflow/dags/intro_dag.py or C:\Users\YourUsername\airflow\dags\intro_dag.py. Ensure it’s .py (Windows: “Save As,” “All Files,” intro_dag.py).

Step 3: Verify and Run the DAG

1. Check the UI: Open your browser, go to localhost:8080, wait 10-20 seconds, and see “intro_dag” listed.
2. Trigger the DAG: In your terminal, activate, type airflow dags trigger -e 2025-04-07 intro_dag, and press Enter—it runs start, then end.
3. View Results: Refresh localhost:8080, click “intro_dag”—see green circles for “start” and “end,” with logs in Task Logging and Monitoring).

This DAG echoes messages daily—your first workflow is live!

Key Features of DAGs

DAGs come with features that make them powerful.

Dependencies

Set with >>—e.g., start >> end—ensuring end waits for start. Use lists for multiple dependencies—start >> [task2, task3]—detailed in DAG Dependencies and Task Ordering.

Scheduling

The schedule_interval—like @daily—automates runs. Use cron (0 0 * * *) or presets—customize with DAG Scheduling (Cron, Timetables).

Catchup and Backfill

Set catchup=True to run past dates from start_date—e.g., January to April 2025—or False to start now (Catchup and Backfill Scheduling).

Best Practices for DAGs

Keep DAGs clean—use unique dag_ids, set start_date in the past, and avoid loops. Store them in ~/airflow/dags—organize with subfolders if needed (DAG File Structure Best Practices). Test with airflow dags test (DAG Testing with Python) before scheduling.

FAQ: Common Questions About DAGs in Airflow

Here are frequent questions about DAGs, with detailed answers from online sources.

1. Why does my DAG not show up in the Airflow UI?

It’s likely not in ~/airflow/dags—check with ls -a ~/airflow/dags (Mac/Linux) or dir %userprofile%\airflow\dags (Windows). Ensure no syntax errors—run python ~/airflow/dags/my_dag.py to test. The Scheduler must be running—type airflow scheduler if not (Airflow CLI: Overview and Usage). Wait 10-20 seconds after saving—adjust dag_dir_list_interval in Airflow Configuration Options.

2. What happens if I don’t set a schedule_interval in my DAG?

It won’t run automatically—set schedule_interval=None and trigger manually with airflow dags trigger -e 2025-04-07 my_dag (Triggering DAGs via UI). Add @daily or cron (0 0 * * *) for automation—see DAG Scheduling (Cron, Timetables).

3. How do I test a DAG without running it live?

Use airflow dags test my_dag 2025-04-07—type it and press Enter to simulate the DAG for that date, showing output without database changes. Test tasks with airflow tasks test my_dag task1 2025-04-07—it’s dry-run mode, perfect for debugging (DAG Testing with Python).

4. Can I have multiple tasks run at the same time in a DAG?

Yes—if tasks lack dependencies (e.g., task1 >> [task2, task3]), they run in parallel with LocalExecutor or CeleryExecutor (Airflow Executors (Sequential, Local, Celery)). SequentialExecutor runs one-by-one—tune with Task Concurrency and Parallelism.

5. What’s the difference between start_date and schedule_interval?

start_date (e.g., datetime(2025, 1, 1)) is when the DAG begins—Airflow calculates runs from there. schedule_interval (e.g., @daily) is how often it runs—daily from January 1, 2025. Together, they set the timeline—catchup=True runs past dates (Catchup and Backfill Scheduling).

6. Why can’t I create a DAG with a loop?

Loops—like task1 >> task2 >> task1—break the “acyclic” rule, causing infinite runs. Airflow prevents this—use task1 >> task2 >> task3 for a finite flow. It’s by design for predictable workflows (DAG Dependencies and Task Ordering).

7. How do I pause a DAG so it stops running automatically?

Type airflow dags pause my_dag and press Enter—it stops scheduling but keeps past runs. Unpause with airflow dags unpause my_dag—manage with Pause and Resume DAGs. Check status in Monitoring Task Status in UI).

Conclusion

DAGs are Airflow’s foundation—defining workflows with tasks and dependencies. Set up Airflow with Installing Airflow (Local, Docker, Cloud), craft DAGs in Defining DAGs in Python, and optimize with Airflow Performance Tuning. Monitor in Monitoring Task Status in UI) and explore more with Airflow Concepts: DAGs, Tasks, and Workflows!