1. Introduction: The Importance of Autoscaling

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.

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2. The Contenders: Understanding the Basics

Horizontal Pod Autoscaler (HPA)

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.

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Vertical Pod Autoscaler (VPA)

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.

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3. The Limitations: When Vanilla Kubernetes Autoscalers Fall Short

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:

• Message Queues: Applications might need to scale based on the number of messages in a queue (like RabbitMQ or Kafka). If there’s a surge of unprocessed messages, it might be an indicator to scale up.
• Database Triggers: Changes or updates in a database (like a sudden increase in rows of a particular table) might necessitate an application scale-up to process or analyze the influx of