AI & Automation

QueueLag

Distributed queue backlogs often trigger silent SLA breaches and runaway operational delays before engineering teams can assess the financial impact. QueueLag tracks queue processing velocities, calculates the economic cost of delays, and projects exact consumer scaling targets to prevent threshold breaches. ## Key features - **Predictive Breach & Drain Modeling**: Calculates net backlog growth rates from sliding-window snapshots to forecast seconds until SLA violation or full queue drain. - **Economic Delay Cost Engine**: Translates pending queue lag into estimated hourly financial losses, including surcharge multipliers during active SLA breaches. - **Consumer Capacity Recommender**: Computes the exact worker count required to absorb current arrival rates and drain backlogs within defined SLA windows. - **Multi-Format Observability Server**: Serves real-time queue health through an integrated HTTP server supporting Prometheus metrics (`/metrics`) and JSON (`/api/status`). ## Example ```bash python queuelag.py --help python queuelag.py --port 8080 ``` ## Limitations QueueLag operates purely on sliding-window in-memory metric snapshots or synthetic simulations and does not natively connect to message brokers like Kafka or RabbitMQ to pull metrics or execute actual autoscaling actions.

python queuelag.py --help