If hundreds of thousands of DAGs are triggered simultaneously, the system might crash. How would you design the system to handle this load?

To handle a massive surge of DAG runs, I would architect Airflow with horizontal scalability and resource isolation. First, use the CeleryExecutor or KubernetesExecutor so that each worker runs in its own pod or container, allowing independent scaling. Configure a worker pool per DAG or per group of DAGs to limit concurrency and prevent a single heavy DAG from starving others. Leverage Airflow’s DAG concurrency and max_active_runs_per_dag settings to cap the number of simultaneous runs per DAG. For the scheduler, enable the scheduler queue and use the scheduler’s parallelism to distribute tasks across workers. Implement dynamic DAG generation: instead of loading all DAGs at startup, use DAG serialization and the DAG bag to load only the DAGs that are scheduled to run. Use a message queue (e.g., RabbitMQ or Redis) to buffer DAG run requests, allowing workers to pull jobs at a controlled rate. Autoscale the worker fleet based on queue depth or CPU usage, ensuring that new workers spin up when the queue grows. Monitor key metrics (queue length, worker latency, task failures) and set alerts. Finally, consider partitioning the Airflow deployment: run critical DAGs on a dedicated Airflow instance with higher resources, while less critical DAGs share a secondary instance. This layered approach balances load, isolates failures, and keeps the system responsive even under extreme concurrency.