In today's cloud-native ecosystem, fluctuating workloads and dynamic traffic patterns are the norms. Accommodating such unpredictable behavior requires systems that can adjust in real-time. Autoscaling is a necessity, ensuring optimal resource allocation, curbing excessive costs, and fostering efficient resource use.
Autoscaling isn't just about costs. It plays a pivotal role in maintaining application performance and throughput. By avoiding both under-provisioning (leading to poor user experience) and over-provisioning (resulting in unnecessary costs), autoscaling strikes the right balance.
HPA, as Kubernetes' native solution, scales the number of pods based on observed metrics, primarily CPU and memory. While it’s straightforward and beneficial for uniform workloads, its limitations become evident when you consider its inability to scale to zero and reliance solely on CPU and memory metrics.
VPA is more about adjusting resources than expanding them. It gauges the demand and adapts resources dynamically, ensuring the right fit for a workload. But here's the catch: a beefed-up pod isn't necessarily better. Sometimes, having more workers process data is more efficient than having one large, powerful worker.
While built-in Kubernetes autoscalers like HPA and VPA provide basic scaling capabilities, they are inherently limited in their scope. Their primary focus on CPU and memory metrics can be a significant limitation for modern applications that might need to react to diverse metrics, some of which might not even emanate from the application itself.
One of the compelling challenges modern applications face is the need to scale in response to events from external systems. For instance:
Moreover, there are instances where scaling to zero is vital to manage resources efficiently, or scenarios where a combination of different metrics, perhaps CPU utilization coupled with database read/write rates, determines the scaling logic. These nuanced demands highlight the shortcomings of built-in Kubernetes autoscalers.
Kubernetes introduced an interface for custom metrics aiming to offer the Horizontal Pod Autoscaler (HPA) more adaptability beyond just CPU and memory metrics. However, practical implementation has surfaced challenges.
While robust, the custom metrics API is not intuitively user-friendly. It demands a detailed grasp of Kubernetes internals, making setup and adjustments cumbersome.
Prometheus Adapter attempts to bridge this gap by tapping into the custom metrics API, bringing in Prometheus' extensive metrics. But it comes with its baggage: a complex, non-intuitive configuration and being tied only to Prometheus metrics. Implementing and upkeeping the configuration demands constant vigilance. Infrastructure or application changes can trigger the need for reconfigurations.
Kubernetes Event-Driven Autoscaling (KEDA) doesn't just integrate with Kubernetes' custom metrics API—it makes it accessible. It's a testament to how user-friendly interfaces can transform an experience, making autoscaling truly customizable and versatile.
KEDA offers multiple technical advantages:
While traditional metrics such as CPU and memory provide some insights, real-world applications often demand more granular and diverse indicators for effective autoscaling. Here are some scenarios to consider:
Such varied real-world scenarios emphasize the need for a versatile autoscaling solution that can understand and react to a multitude of metrics. KEDA, with its flexibility and adaptability, addresses these challenges effectively.
While Kubernetes has native autoscaling tools like HPA and VPA, and extensions like the Prometheus Adapter, they often come with complexities. KEDA, on the other hand, provides a straightforward platform for diverse autoscaling needs. Its ability to handle event-driven scaling, including scaling to zero, is a significant advantage. Moreover, setting up KEDA is simpler, reducing the typical hurdles users face with Kubernetes' custom metrics.
KEDA's active community is a testament to its utility. Regular contributions to the project, vendors like Kedify or Microsoft and increasing adoption among businesses show its growing importance in the Kubernetes ecosystem.
Learn how Kedify can help you cut your cloud costsJoin the waitlist