Why Default Kubernetes HPA Fail for Modern Workloads And Why We Switched to KEDA
While Kubernetes' built-in HPA works well for basic CPU and memory metrics, it falls short in modern, event-driven architectures. Traditional HPA relies on reactive resource utilization thresholds, often scaling up too late after a system is already under load, and cannot scale down to zero when idle.
By transitioning to Kubernetes Event-driven Autoscaling (KEDA), we move from reactive resource monitoring to proactive, event-driven scaling based on actual application demand, such as message queue depth, HTTP requests, Response rate, or custom database metrics, while enabling scale-to-zero efficiency to cut infrastructure costs.
The Problem: Default HPA Limitations
Key Failure Modes:
1. Slow HPA Reaction (Reactive Scaling):
[Traffic Spike] --> [High Latency] --> [Overwhelmed Pods]
2. Inability to Scale to Zero (Cost Inefficiency):
[No Traffic] --> [Pods Stay Active] --> [Idle Compute Costs]
When building event-driven architectures, system design ultimately hinges on two critical priorities: cost efficiency and operational stability. Relying solely on...
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