Principle:Apache Airflow Monitoring Operations
| Knowledge Sources | |
|---|---|
| Domains | Resource_Management, Monitoring |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
A resource management and monitoring system for controlling task concurrency and tracking DAG execution health.
Description
Monitoring Operations in Airflow encompass resource pools for concurrency control, callbacks for event notification, and deadline/SLA tracking. Pools limit the number of tasks that can run concurrently across DAGs, preventing resource exhaustion. DAG-level callbacks (on_success, on_failure) provide hooks for custom notification logic. Deadlines and SLA monitoring alert operators when task execution exceeds expected timeframes.
Usage
Use pools when tasks access shared resources with limited capacity (e.g., database connections, API rate limits). Configure callbacks for alerting on DAG failures. Set deadlines for time-sensitive workflows that require prompt execution.
Theoretical Basis
Pool-based Concurrency Control:
- Each pool has a fixed number of slots (-1 for unlimited)
- Tasks specify their pool and pool_slots requirement
- The scheduler only dispatches tasks when sufficient pool slots are available
- Deferred tasks can optionally count against pool slots (include_deferred)
Callback Model:
# Pseudo-code for DAG state change callbacks
def on_dag_state_change(dag, dagrun, new_state):
if new_state == SUCCESS and dag.on_success_callback:
for callback in dag.on_success_callback:
callback(context)
elif new_state == FAILED and dag.on_failure_callback:
for callback in dag.on_failure_callback:
callback(context)