In the ever-evolving landscape of container orchestration and deployment, Kubernetes stands as a powerhouse. However, as applications become more complex, the need for dynamic and efficient scaling mechanisms becomes imperative. This is where Kubernetes Event-Driven Autoscaling (KEDA) steps in, providing a solution that seamlessly adapts to varying workloads and enhances resource utilization. In this article, we will explore the fundamental concepts, benefits, and practical implementation of KEDA in Kubernetes.
KEDA is an open-source project designed to extend Kubernetes’ native horizontal pod autoscaling capabilities. Unlike traditional autoscaling, which relies on CPU or memory metrics, KEDA introduces the ability to scale based on custom metrics and external events. This enables applications to dynamically adjust their resource allocation in response to specific triggers, leading to improved efficiency and cost-effectiveness.
Event-Driven Scaling: KEDA allows scaling based on events such as messages in a queue, records in a database, or any other custom metric. This flexibility enables applications to scale precisely in response to real-time demands. Support for Various Event Sources: KEDA supports a wide range of event sources, including Azure Queue, RabbitMQ, Kafka, GCP Pub/Sub, AWS SNS, Prometheus and more. This makes it compatible with diverse workloads and systems. Native Integration with Kubernetes: KEDA seamlessly integrates with Kubernetes, leveraging the Kubernetes Custom Resource Definitions (CRDs) to extend the native scaling capabilities. This ensures that KEDA operates smoothly within Kubernetes environments without introducing complexity. At a high level, KEDA provides below components in order to control the autoscaling process:
Operator / Agent : Activates and deactivates Kubernetes Deployments to scale to and from zero on no events.
Connects to an external event source and feed custom metrics for a specific event source. A current list of scalers is available on the KEDA home page. ( https://keda.sh/#scalers )
Acts as a Kubernetes metrics server that exposes rich event data like queue length or stream lag to the Horizontal Pod Autoscaler to drive scale out.