Building a Custom Metrics Exporter for Kubernetes
Kubernetes ships with built-in awareness of CPU and memory, but most real-world scaling decisions depend on signals outside that narrow window, such as queue depth, batch job duration, or active WebSocket connections. When built-in metrics are not enough, a metrics exporter bridges that gap. This post walks through writing one from scratch, packaging it as a container, and wiring it into a cluster so that Prometheus—and ultimately the HorizontalPodAutoscaler—can consume it. An exporter is a small HTTP server that exposes application state as text on a /metrics endpoint. Prometheus scrapes that endpoint on a regular interval, stores the time-series data, and makes it available for queries, alerts, and autoscaling rules. In some cases, you can instrument your application directly by embedding the Prometheus client library and exposing /metrics from within the same process, rather than running a separate exporter. A standalone exporter makes more sense when the data source is external to your application or when you do not control the application code.
Custom metrics enable autoscaling based on real-world signals beyond CPU and memory.