⚙️ Scenario

You have a cleanup service (e.g., deleting old files, cleaning temp data, or archiving logs) that should run periodically.

You can schedule it using either:

• Kubernetes CronJob → native K8s scheduling
• Airflow DAG → workflow orchestration

---

1️⃣ Kubernetes CronJob

Definition:

A CronJob in Kubernetes runs a Job on a schedule, similar to Linux cron.

How It Works:

1. Define a CronJob YAML:

   apiVersion: batch/v1
   kind: CronJob
   metadata:
     name: cleanup-cron
   spec:
     schedule: "0 2 * * *" # every day at 2 AM
     jobTemplate:
       spec:
         template:
           spec:
             containers:
             - name: cleanup
               image: my-cleanup-service:latest
             restartPolicy: OnFailure
   

2. K8s scheduler triggers a Job container at the scheduled time.

3. Job runs the cleanup → deletes old resources or temp data.

4. Can scale horizontally if needed (multiple jobs run concurrently).

Pros:

• Simple, declarative
• Native K8s scheduling → no external tool
• Handles retries via restartPolicy

Cons:

• Limited to simple cron scheduling
• No dependency management between jobs

---

2️⃣ Airflow DAG (Directed Acyclic Graph)

Definition:

Airflow schedules tasks (Operators) in DAGs with complex dependencies, retries, and logging.

How It Works:

1. Define a DAG for cleanup:

   from airflow import DAG
   from airflow.operators.python import PythonOperator
   from datetime import datetime, timedelta
   
   def cleanup():
       # logic to delete old files
       pass
   
   dag = DAG(
       'cleanup_dag',
       start_date=datetime(2025,10,10),
       schedule_interval='0 2 * * *',  # daily at 2 AM
       catchup=False
   )
   
   cleanup_task = PythonOperator(
       task_id='cleanup_task',
       python_callable=cleanup,
       dag=dag
   )
   

2. Airflow scheduler triggers the task at scheduled time.

3. Supports retries, notifications, dependencies, and logging.

Pros:

• Powerful scheduling & dependency management
• Easy retries and alerting
• Good for complex workflows

Cons:

• More infrastructure overhead (Airflow server, DB, scheduler)
• Overkill for simple periodic tasks

---

🔄 Comparison Table

Feature	Kubernetes CronJob	Airflow DAG
Complexity	Simple	Advanced
Dependencies	None	Yes, DAG-based
Retries	Yes, restartPolicy	Yes, configurable
Logging / Monitoring	Basic	Extensive, web UI
Infrastructure	K8s cluster only	Airflow server + DB
Use Case	Simple periodic jobs	Complex workflows with dependencies

---

🧩 Analogy

• CronJob: “Set an alarm clock to run cleanup at 2 AM” → simple, reliable
• Airflow: “Set a workflow schedule with multiple steps, retry on failure, notify if something goes wrong” → more control

---

✅ Key Takeaways

1. K8s CronJob → lightweight, good for simple periodic tasks (cleanup, reports)
2. Airflow DAG → heavyweight, better for complex pipelines with dependencies and retries
3. Both can trigger cleanup services, but choice depends on complexity, monitoring, and dependencies

---