kubernetesobservabilityPractitioner

Building a Custom Metrics Exporter for Kubernetes: A Practical Guide

5 min read Kubernetes BlogJul 14, 2026Reviewed for accuracy

Practitioner — Hands-on experience recommended

In Kubernetes, monitoring your applications is crucial for maintaining performance and reliability. Custom metrics exporters allow you to expose application state through a /metrics endpoint, making it easier for Prometheus to scrape and analyze your data. This capability is vital for understanding how your applications behave under load and can inform decisions on scaling and resource allocation.

A metrics exporter is essentially a small HTTP server that serves metrics in a format Prometheus can understand. You’ll typically use counters for totals, gauges for current values, and histograms for distributions. Prometheus scrapes the /metrics endpoint at regular intervals, storing the time-series data for queries, alerts, and autoscaling rules. When setting up your exporter, ensure you register your metrics correctly to avoid issues. For example, prometheus.MustRegister will panic on duplicate registrations, making misconfigurations evident at startup rather than silently failing at runtime.

In production, you need to consider security and performance. Using a distroless image, like distroless/static:nonroot, helps you comply with security policies without additional configuration. Automating your build process with a CI/CD pipeline is also advisable to avoid manual errors. Remember, the Go Prometheus client is the go-to choice for building exporters in Kubernetes, so familiarize yourself with it to streamline your development process.

Key takeaways

→Expose application metrics through a /metrics endpoint for Prometheus scraping.
→Use counters for totals, gauges for current values, and histograms for distributions.
→Register metrics correctly to avoid startup panics with prometheus.MustRegister.
→Utilize distroless images for enhanced security in Kubernetes environments.
→Automate builds with CI/CD pipelines to reduce manual errors.

Why it matters

Implementing a custom metrics exporter enables you to monitor application performance effectively, leading to better resource management and improved reliability in production environments.

Code examples

Dockerfile

1FROM golang:1.21-alpine AS builder
2WORKDIR /src
3COPY go.mod go.sum ./
4RUN go mod download
5COPY . .
6RUN CGO_ENABLED=0 go build -o /exporter .
7FROM gcr.io/distroless/static:nonroot
8COPY --from=builder /exporter /exporter
9EXPOSE 8080
10ENTRYPOINT ["/exporter"]

YAML

1apiVersion: apps/v1
2kind: Deployment
3metadata:
4  name: my-exporter
5  namespace: monitoring
6  labels:
7    app.kubernetes.io/name: my-exporter
8spec:
9  replicas: 1
10  selector:
11    matchLabels:
12      app.kubernetes.io/name: my-exporter
13  template:
14    metadata:
15      labels:
16        app.kubernetes.io/name: my-exporter
17    spec:
18      containers:
19      - name: exporter
20        image: <registry>/my-exporter:v1.0.0
21        ports:
22        - name: metrics
23          containerPort: 8080
24        livenessProbe:
25          httpGet:
26            path: /healthz
27            port: 8080
28          initialDelaySeconds: 5
29          periodSeconds: 10
30        resources:
31          requests:
32            cpu: 50m
33            memory: 32Mi
34          limits:
35            cpu: 100m
36            memory: 64Mi
37---
38apiVersion: v1
39kind: Service
40metadata:
41  name: my-exporter
42  namespace: monitoring
43  labels:
44    app.kubernetes.io/name: my-exporter
45spec:
46  selector:
47    app.kubernetes.io/name: my-exporter
48  ports:
49  - name: metrics
50    port: 8080
51    targetPort: metrics
52

YAML

1apiVersion: monitoring.coreos.com/v1
2kind: ServiceMonitor
3metadata:
4  name: my-exporter
5  namespace: monitoring
6  labels:
7    release: kube-prometheus-stack
8spec:
9  selector:
10    matchLabels:
11      app.kubernetes.io/name: my-exporter
12  endpoints:
13  - port: metrics
14    interval: 15s
15    path: /metrics