Pause and Resume DAGs

What is Pause and Resume DAGs in Airflow?

Pausing and resuming DAGs in Airflow refers to toggling a DAG’s active state, controlling whether the Scheduler triggers new runs based on its schedule_interval (Schedule Interval Configuration). A paused DAG—set to “Off” in the UI or via CLI—stops scheduling new runs, though any currently running or queued tasks complete their execution. Resuming—switching to “On”—reactivates scheduling, picking up from the next scheduled interval (or backfilling if catchup=True (Catchup and Backfill Scheduling)). Managed by the Scheduler (Airflow Architecture (Scheduler, Webserver, Executor)), this state is stored in the metadata database and checked during scans of the ~/airflow/dags directory (DAG File Structure Best Practices). Tasks execute via the Executor (Airflow Executors (Sequential, Local, Celery)), logs track activity (Task Logging and Monitoring), and the UI reflects the DAG’s status (Airflow Graph View Explained). Pausing and resuming provide a simple yet powerful way to manage workflow execution dynamically, balancing automation with manual oversight.

Key Mechanics

• Paused State (“Off”): No new runs are scheduled; existing runs finish.
• Active State (“On”): Scheduling resumes per schedule_interval.
• Toggle Methods: UI toggle, CLI (airflow dags pause/resume), or API.
• Database Flag: Stored as is_paused in the dag table.

Why Pause and Resume DAGs Matter in Airflow

Pausing and resuming DAGs are essential because they give you granular control over workflow execution, addressing operational needs like maintenance, debugging, or resource management. Without this capability, a misbehaving DAG could continue running, consuming resources or producing errors, with no easy way to stop it short of deleting the file or killing processes—risking data loss. They integrate with Airflow’s scheduling features—supporting cron (Cron Expressions in Airflow), variables (Dynamic Scheduling with Variables), and time zones (Time Zones in Airflow Scheduling)—allowing you to pause during backfills (Catchup and Backfill Scheduling) or dynamic DAG updates (Dynamic DAG Generation). For example, you might pause a DAG during a database outage, then resume once resolved, or halt a resource-intensive pipeline during peak hours. This flexibility prevents waste, ensures stability, and supports iterative development, making Airflow a more practical tool for real-world workflows.

Common Scenarios

• Maintenance Windows: Pause during system updates.
• Debugging: Halt runs to fix errors without interference.
• Resource Management: Stop heavy DAGs during high load.
• Testing: Pause to adjust logic, then resume.

How Pause and Resume DAGs Work in Airflow

Pausing and resuming DAGs hinge on the is_paused flag in the metadata database, toggled via the UI, CLI, or API. When a DAG is active (“On”), the Scheduler checks its schedule_interval during each scan of the dags folder (frequency set by dag_dir_list_interval in airflow.cfg (Airflow Configuration Basics), scheduling runs as intervals pass—e.g., daily at midnight for "0 0 * * *". Pausing sets is_paused=True, stopping new runs from being queued, though in-flight tasks (running or queued) complete via the Executor. Resuming flips is_paused=False, and the Scheduler resumes from the next interval after the last completed run (or backfills if catchup=True). For instance, pausing a daily DAG on April 7, 2025, at 10:00 UTC, then resuming April 9, skips April 8’s run unless catchup is enabled. Logs reflect task states (DAG Serialization in Airflow), and the UI updates the toggle. This mechanism ensures controlled interruptions, preserving workflow integrity.

Using Pause and Resume DAGs in Airflow

Let’s create a DAG, pause it, and resume it, with detailed steps.

Step 1: Set Up Your Airflow Environment

1. Install Airflow: Open your terminal, navigate to your home directory (cd ~), and create a virtual environment (python -m venv airflow_env). Activate it—source airflow_env/bin/activate on Mac/Linux or airflow_env\Scripts\activate on Windows—then install Airflow (pip install apache-airflow) for a clean setup.
2. Initialize the Database: Run airflow db init to create the metadata database at ~/airflow/airflow.db, storing the is_paused state.
3. Start Airflow Services: In one terminal, activate the environment and run airflow webserver -p 8080 for the UI at localhost:8080. In another, run airflow scheduler to manage DAG states (Installing Airflow (Local, Docker, Cloud)).

Step 2: Create a DAG to Pause and Resume

1. Open a Text Editor: Use Visual Studio Code, Notepad, or any plain-text editor—ensure .py output.
2. Write the DAG Script: Define a daily DAG. Here’s an example:

• Copy this code:

Copy codefrom airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime

def pause_resume_task(ds):
    print(f"Running for {ds} - can be paused or resumed!")

with DAG(
    dag_id="pause_resume_dag",
    start_date=datetime(2025, 1, 1),
    schedule_interval="0 0 * * *",  # Midnight UTC daily
    catchup=False,
) as dag:
    task = PythonOperator(
        task_id="pause_resume_task",
        python_callable=pause_resume_task,
        op_kwargs={"ds": "{ { ds } }"},
    )

• Save as pause_resume_dag.py in ~/airflow/dags—e.g., /home/user/airflow/dags/pause_resume_dag.py on Linux/Mac or C:\Users\YourUsername\airflow\dags\pause_resume_dag.py on Windows. Use “Save As,” select “All Files,” and type the full filename.

Step 3: Test, Pause, and Resume the DAG

1. Test the DAG: Run airflow dags test pause_resume_dag 2025-04-07 to simulate April 7, 2025, printing “Running for 2025-04-07 - can be paused or resumed!”—validating the setup (DAG Testing with Python).
2. Activate the DAG: On April 7, 2025 (system date), go to localhost:8080, find “pause_resume_dag,” and toggle it “On.” It schedules April 8, 2025, at 00:00 UTC. Check “Runs” for “scheduled” status (Airflow Web UI Overview).
3. Pause the DAG: In the UI, toggle “pause_resume_dag” to “Off” at 10:00 UTC on April 7. No new runs queue (April 8 won’t start), but any running April 7 tasks finish. Verify in “Runs”—no new “scheduled” entries.
4. Resume the DAG: On April 9 at 10:00 UTC, toggle it “On.” With catchup=False, it schedules April 10, 2025, at 00:00 UTC, skipping April 8-9. Check logs post-run for “Running for 2025-04-09”.

This demonstrates pausing and resuming a daily DAG, controlling its schedule.

Key Features of Pause and Resume DAGs in Airflow

Pause and resume functionality offers versatile control.

UI Toggle Control

Pause/resume via the web UI’s toggle switch.

Example: UI Pause

Use the DAG above. Toggle “Off” mid-day—stops scheduling; toggle “On” later—resumes from the next interval.

CLI Pause and Resume

Use CLI commands for automation or scripting.

Example: CLI Control

Copy codefrom airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime

def cli_task(ds):
    print(f"CLI-managed run for {ds}")

with DAG(
    dag_id="cli_pause_dag",
    start_date=datetime(2025, 1, 1),
    schedule_interval="0 12 * * *",  # Noon UTC
    catchup=False,
) as dag:
    task = PythonOperator(task_id="cli_task", python_callable=cli_task, op_kwargs={"ds": "{ { ds } }"})

Pause: airflow dags pause cli_pause_dag—stops noon runs. Resume: airflow dags unpause cli_pause_dag—restarts next noon.

Handling Running Tasks

Paused DAGs let running tasks complete.

Example: Long-Running Task

Copy codefrom airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime
import time

def long_task(ds):
    print(f"Starting long task for {ds}")
    time.sleep(300)  # 5 minutes
    print("Long task done")

with DAG(
    dag_id="long_running_dag",
    start_date=datetime(2025, 1, 1),
    schedule_interval="0 0 * * *",
    catchup=False,
) as dag:
    task = PythonOperator(task_id="long_task", python_callable=long_task, op_kwargs={"ds": "{ { ds } }"})

Trigger April 7, pause at 00:02 UTC—task runs 5 minutes, finishes at 00:05, no new runs queue.

Catchup on Resume

Resume with catchup for missed intervals.

Example: Catchup Resume

Copy codefrom airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime

def catchup_task(ds):
    print(f"Catching up for {ds}")

with DAG(
    dag_id="catchup_pause_dag",
    start_date=datetime(2025, 1, 1),
    schedule_interval="0 0 * * *",
    catchup=True,
) as dag:
    task = PythonOperator(task_id="catchup_task", python_callable=catchup_task, op_kwargs={"ds": "{ { ds } }"})

Pause April 7, resume April 9—runs January 1 to April 8, then continues (Catchup and Backfill Scheduling).

Dynamic Pause with Variables

Pause programmatically via variables.

Example: Variable-Driven Pause

Copy codefrom airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.models import Variable
from datetime import datetime

def var_task(ds):
    print(f"Variable-controlled run for {ds}")

schedule = "0 0 * * *" if Variable.get("dag_active", default_var="true").lower() == "true" else None

with DAG(
    dag_id="var_pause_dag",
    start_date=datetime(2025, 1, 1),
    schedule_interval=schedule,
    catchup=False,
) as dag:
    task = PythonOperator(task_id="var_task", python_callable=var_task, op_kwargs={"ds": "{ { ds } }"})

Set dag_active to "false" (airflow variables set dag_active false)—effectively pauses by nullifying the schedule (Dynamic Scheduling with Variables).

Best Practices for Pause and Resume DAGs in Airflow

Optimize pausing and resuming with these detailed guidelines:

• Pause Before Changes: Pause DAGs during code updates to avoid mid-run conflicts—resume after deployment.
• Check Running Tasks: Before pausing, review “Running” states in the UI—let critical tasks finish to avoid data loss Airflow Web UI Overview.
• Use Catchup Wisely: Set catchup=False unless backfilling missed runs is intentional—prevents unexpected load on resume.
• Automate with CLI: Script pauses/resumes (e.g., airflow dags pause my_dag) for maintenance windows—log actions for audit.
• Monitor Post-Resume: Check logs after resuming for queued runs or errors—e.g., “Task failed” from overlap Task Logging and Monitoring.
• Document State Changes: Note pause/resume reasons in comments—e.g., # Paused for DB maintenance—for team clarity DAG File Structure Best Practices.
• Limit Active Runs: Use max_active_runs (e.g., 2) to control catchup load on resume—eases resource strain Airflow Performance Tuning.
• Test Pause Impact: Simulate with airflow dags test and pause mid-run to ensure task completion behavior.

These practices ensure controlled, efficient DAG management.

FAQ: Common Questions About Pause and Resume DAGs in Airflow

Here’s an expanded set of answers to frequent questions from Airflow users.

1. Why don’t my DAG runs stop immediately when paused?

Pausing prevents new runs—existing running or queued tasks finish. Check “Running” status before assuming it’s stopped (Airflow Web UI Overview).

2. How do I pause a DAG programmatically?

Use CLI (airflow dags pause my_dag) or API—e.g., PATCH /dags/{dag_id} with {"is_paused": true}—for automation.

3. What happens to missed runs when I resume?

With catchup=False, it skips missed intervals, starting from the next. With catchup=True, it backfills all missed runs (Catchup and Backfill Scheduling).

4. Why does my DAG still show as “Running” after pausing?

In-flight tasks continue—pause only stops new scheduling. Wait for tasks to complete or kill them via UI/CLI (Task Logging and Monitoring).

5. Can I pause during a backfill?

Yes—toggle “Off” mid-backfill; running tasks finish, new ones halt. Resume to continue from the last completed interval.

6. How do I test pausing without affecting live runs?

Use airflow dags test my_dag 2025-04-07, pause via CLI (airflow dags pause my_dag), and check behavior—dry runs don’t impact production (DAG Testing with Python).

7. Why does resuming trigger too many runs?

catchup=True backfills all missed intervals—e.g., a month paused = 30 daily runs. Set catchup=False or limit with max_active_runs (Airflow Performance Tuning).

8. Can I resume a DAG from a specific date?

Not directly—resume picks the next interval or backfills. Use airflow dags backfill -s <date></date> post-resume for custom starts.

Conclusion

Pausing and resuming DAGs offer essential control in Airflow—set them up with Installing Airflow (Local, Docker, Cloud), craft DAGs via Defining DAGs in Python, and monitor with Monitoring Task Status in UI. Explore more with Airflow Concepts: DAGs, Tasks, and Workflows and Schedule Interval Configuration!