Building a Custom Metrics Exporter for Kubernetes: A Practical Guide
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
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"]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
521apiVersion: 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: /metricsWhen NOT to use this
The official docs don't call out specific anti-patterns here. Use your judgment based on your scale and requirements.
Want the complete reference?
Read official docsIndustry-standard certifications built by the people behind Linux and Kubernetes. Earn the CKA — the gold standard Kubernetes administrator cert. OpsCanary readers get 30% off year-round with code OPSCANARY3.
Get CKA certified →Kubernetes v1.37: Metrics API Stabilization and Its Impact
Kubernetes v1.37 has promoted the metrics.k8s.io API to stable, a crucial step for monitoring resource usage in your clusters. This API provides real-time CPU and memory metrics for nodes and Pods, enabling effective autoscaling and performance tuning.
The Lazy Developer’s Guide to Observing Your Code with OpenTelemetry
Observability is crucial for maintaining healthy applications, yet many developers shy away from it. With zero-code instrumentation, you can add observability without touching your source code. This guide will show you how to leverage OpenTelemetry effectively.
Automating RCA at Scale: Mastering Multi-Signal Correlation in Kubernetes
Root Cause Analysis (RCA) can be a nightmare in cloud-native environments. Multi-signal correlation offers a structured approach to pinpoint issues by analyzing anomalies across various signals. This article dives into how this method works and what you need to implement it effectively.
Get the daily digest
One email. 5 articles. Every morning.
No spam. Unsubscribe anytime.