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Hey everyone!

I'm looking to implement some best practices for using Airflow at Spotify, especially concerning DAG organization, task idempotency, and efficient resource utilization. We're dealing with large-scale data pipelines and need to ensure reliability and scalability. Any insights or shared experiences from anyone working with Airflow in a similar environment would be greatly appreciated!

Specifically, I'm interested in:

• DAG folder structure for monorepos.
• Strategies for handling large numbers of tasks and DAGs.
• Recommended patterns for idempotency in custom operators.
• Best ways to monitor and debug complex pipelines.

Thanks in advance!

Great question, DataEngineer101! Spotify's scale definitely presents unique challenges.

For DAG organization, many teams use a feature-based or domain-based structure. Think `dags/analytics/user_engagement` or `dags/recommendations/song_processing`. Within each, you can have subfolders for `operators`, `hooks`, `sensors`, etc., to keep things modular.

Regarding idempotency, always ensure your operators are designed to be re-runnable. This often involves checking for existing results before performing an action or using transactional data loading techniques. Using `task_id` prefixes or suffixes related to the operation can also help.

For monitoring, Datadog or similar tools integrated with Airflow's logs and metrics are crucial. Setting up custom alerts for task failures, long-running tasks, or resource spikes is a must.

Echoing PipelineMaven's points. On the scaling front, consider dynamic DAG generation for repetitive structures, and explore using CeleryExecutor or KubernetesExecutor for distributed task execution. Airflow's own documentation has a good section on scaling.

For large numbers of tasks, consider breaking down monolithic DAGs into smaller, more manageable ones that can be triggered by a parent DAG. This improves readability and makes debugging easier. Also, leverage `ExternalTaskSensor` or `TriggerDagRunOperator` for inter-DAG communication.