Скачать или смотреть Creating Custom Metrics for Autoscaling

Custom metrics provide more granular control over autoscaling by allowing you to define scaling criteria beyond default CPU and memory usage. This is particularly useful for applications with unique performance indicators, such as request latency or queue length. To create custom metrics, you can use tools like Prometheus to collect and expose these metrics, and integrate them with the Kubernetes Custom Metrics API. Once defined, you can configure your HPA to use these custom metrics for scaling decisions. Testing and validating your custom metrics is crucial to ensure they accurately reflect your application's needs and result in effective scaling. By leveraging custom metrics, you can tailor your autoscaling strategies to better meet your specific operational requirements.