How do you handle task dependencies and retries in Airflow?
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Handling Task DependenciesTask dependencies in Airflow
defined using DAG (Directed Acyclic Graph) relationships.
These can be specified using:
task_1 >> task_2 or task_2 << task_1
task_1.set_downstream(task_2) or task_2.set_upstream(task_1)
These constructs allow you to control which task runs after another. For example, if task_A must complete before task_B starts, you'd write task_A >> task_B. You can also set complex dependencies using lists or branching with BranchPythonOperator.
🔸 Handling Retries
Retries in Airflow are managed at the task level through the following parameters:
retries: Number of retry attempts (e.g., retries=3)
retry_delay: Time interval between retries (e.g., timedelta(minutes=5))
max_retry_delay: Max delay between retries
retry_exponential_backoff: If set to True, delays grow exponentially
retry_timeout: Total retry period before failing
depends_on_past: If True, task will only run if the previous run succeeded
These parameters are passed when defining a task, e.g.:
python
Copy
Edit
PythonOperator(
task_id='extract_data',
python_callable=my_function,
retries=3,
retry_delay=timedelta(minutes=10)
✅ Best Practices
Set depends_on_past=True for sequential data loads.
Use sensors carefully to avoid DAG stalling.
Always monitor DAGs using the Airflow UI or logs.
Together, dependency management and retries ensure tasks execute in the correct sequence and recover gracefully from failures.
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