Event Driven Scaling

Event-Driven Scaling (KEDA)

1. Skill Context

Focus: Kubernetes Event-driven Autoscaling (KEDA), HPA (Horizontal Pod Autoscaler) limitations, scale-to-zero, metric thresholds. Triggers: scale to zero, keda autoscaling, kafka scaling kubernetes, event driven scaling, sqs scaling

2. Advanced Technical Patterns

The agent addresses the limitations of standard CPU/Memory HPA by scaling based on queue depth and external metrics.

KEDA Architecture

• Metrics Adapter: KEDA acts as a Kubernetes Metrics Server, feeding custom metrics (e.g., Kafka lag, RabbitMQ queue length) to the standard HPA controller.
• Scale-to-Zero: The KEDA operator scales deployments from 0 to 1 based on triggers, and the HPA takes over from 1 to N.

Complex Scaling Scenarios

• Kafka Partition-Aware Scaling: Explaining why a consumer group cannot scale effectively beyond the number of Kafka partitions.
• Scaling Hysteresis & Flapping: Configuring cooldownPeriod and pollingInterval to prevent pods from rapidly spinning up and down due to bursty event traffic.
• Long-Running Executions: Scaling worker pods (e.g., video processing) without terminating active jobs. Utilizing Kubernetes Jobs via KEDA's ScaledJob resource instead of ScaledObject (Deployments).

3. Output Format

• Provide robust ScaledObject or ScaledJob YAML definitions.
• Detail the exact trigger authentication (e.g., TriggerAuthentication referencing IAM roles or Secrets).
• Explain metric math (e.g., targetValue = queueLength / acceptableLatency).