OpenTelemetry on Kubernetes: End‑to‑End Tracing, Metrics & Logging Guide

Observability isn’t a buzzword; it’s the lifeline of any modern microservice stack.
When I first tried to stitch together Jaeger, Prometheus, and Loki in a busy cluster, I spent more time hunting missing spans than fixing bugs. The root cause? I was wiring each piece by hand, guessing env vars, and forgetting to propagate context.

This article shows you a single, reproducible way to get OpenTelemetry (OTel) working for tracing, metrics, and logging in Kubernetes. I’ll use:

• OpenTelemetry Collector v0.92.0 (the all‑in‑one binary)
• Helm 3.12 for deployment
• Jaeger 1.45 for traces
• Prometheus 2.48 for metrics
• Grafana Loki 2.9 for logs

You’ll walk away with a Helm chart that you can drop into any cluster (GKE, EKS, AKS, or on‑prem) and a few Terraform snippets to keep it version‑controlled.

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Table of Contents

1. Why a single Collector?
2. Prerequisites
3. Deploy the OpenTelemetry Collector
4. Instrument a sample Go service
5. Configure tracing to Jaeger
6. Export metrics to Prometheus
7. Ship logs to Loki
8. Validate end‑to‑end flow
9. Production tips & gotchas

---

Why a single Collector?

Running three separate agents (OTel sidecar, Prometheus exporter, Loki driver) multiplies CPU usage and configuration drift. The OpenTelemetry Collector can:

• Receive traces, metrics, and logs over OTLP (gRPC or HTTP)
• Transform data (resource detection, attribute renaming)
• Forward to multiple back‑ends in one process

In production I ran the Collector as a DaemonSet on every node, consuming ~30 MiB RAM and <5 % CPU on a 20‑node cluster. That’s a fraction of what three independent agents would cost.

---

Prerequisites

Make sure your kubectl context points to the target cluster and you have cluster‑admin rights to create CRDs.

---

Deploy the OpenTelemetry Collector

1. Add the OTel Helm repo

Bash
helm repo add otel https://open-telemetry.github.io/opentelemetry-helm-charts
helm repo update

2. Create a minimal values.yaml

YAML
# collector-values.yaml
mode: daemonset

config:
  exporters:
    otlphttp:
      endpoint: "http://otel-collector:4318"
    jaeger:
      endpoint: "http://jaeger-collector:14250"
    prometheusremotewrite:
      endpoint: "http://prometheus-server:9090/api/v1/write"
    loki:
      endpoint: "http://loki:3100/loki/api/v1/push"

  processors:
    batch:
      timeout: 10s
      send_batch_max_size: 1024

  receivers:
    otlp:
      protocols:
        grpc:
        http:

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [jaeger]
    metrics:
      receivers: [otlp]
      processors: [batch]
      exporters: [prometheusremotewrite]
    logs:
      receivers: [otlp]
      processors: [batch]
      exporters: [loki]

3. Install the chart

Bash
helm install otel-collector otel/opentelemetry-collector \
  -n observability --create-namespace \
  -f collector-values.yaml \
  --version 0.92.0

The Collector now runs as a DaemonSet named otel-collector-observability. Verify:

Bash
kubectl -n observability get ds otel-collector

You should see one pod per node.

---

Instrument a sample Go service

I’ll use the classic github.com/gin-gonic/gin HTTP server. The only dependency you need is the OTel Go SDK.

Bash
go mod init demo
go get go.opentelemetry.io/otel/sdk@v1.19.0
go get go.opentelemetry.io/otel/exporters/otlp/otlpgrpc@v1.19.0
go get go.opentelemetry.io/contrib/instrumentation/github.com/gin-gonic/gin/otelgin@v0.41.0

main.go

Go
package main

import (
	"context"
	"log"
	"net/http"
	"os"

	"github.com/gin-gonic/gin"
	"go.opentelemetry.io/otel"
	"go.opentelemetry.io/otel/exporters/otlp/otlpgrpc"
	"go.opentelemetry.io/otel/sdk/resource"
	sdktrace "go.opentelemetry.io/otel/sdk/trace"
	semconv "go.opentelemetry.io/otel/semconv/v1.21.0"
	"go.opentelemetry.io/otel/sdk/metric"
	"go.opentelemetry.io/otel/metric/global"
	otelgin "go.opentelemetry.io/contrib/instrumentation/github.com/gin-gonic/gin/otelgin"
)

func initTracer() func(context.Context) error {
	ctx := context.Background()
	exp, err := otlpgrpc.New(ctx,
		otlpgrpc.WithEndpoint(os.Getenv("OTEL_EXPORTER_OTLP_ENDPOINT")),
		otlpgrpc.WithInsecure(),
	)
	if err != nil {
		log.Fatalf("failed to create exporter: %v", err)
	}
	bsp := sdktrace.NewBatchSpanProcessor(exp)
	res, _ := resource.New(ctx,
		resource.WithAttributes(
			semconv.ServiceNameKey.String("demo-go"),
		),
	)
	tp := sdktrace.NewTracerProvider(
		sdktrace.WithSpanProcessor(bsp),
		sdktrace.WithResource(res),
	)
	otel.SetTracerProvider(tp)
	return tp.Shutdown
}

func initMeter() {
	meter := global.MeterProvider().Meter("demo-go")
	// Example: a counter for HTTP requests
	_, _ = meter.Int64Counter("http_requests_total")
}

func main() {
	shutdown := initTracer()
	defer func() {
		if err := shutdown(context.Background()); err != nil {
			log.Fatalf("tracer shutdown error: %v", err)
		}
	}()
	initMeter()

	r := gin.New()
	r.Use(otelgin.Middleware("demo-go"))
	r.GET("/ping", func(c *gin.Context) {
		c.JSON(http.StatusOK, gin.H{"msg": "pong"})
	})
	if err := r.Run(":8080"); err != nil {
		log.Fatalf("server error: %v", err)
	}
}

Build and push the image:

Bash
docker build -t ghcr.io/yourorg/demo-go:0.1 .
docker push ghcr.io/yourorg/demo-go:0.1

Deploy the app

YAML
# demo-go.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: demo-go
  labels:
    app: demo-go
spec:
  replicas: 2
  selector:
    matchLabels:
      app: demo-go
  template:
    metadata:
      labels:
        app: demo-go
      annotations:
        sidecar.opentelemetry.io/inject: "true"
    spec:
      containers:
        - name: demo-go
          image: ghcr.io/yourorg/demo-go:0.1
          ports:
            - containerPort: 8080
          env:
            - name: OTEL_EXPORTER_OTLP_ENDPOINT
              value: "http://otel-collector.observability.svc:4317"
---
apiVersion: v1
kind: Service
metadata:
  name: demo-go
spec:
  selector:
    app: demo-go
  ports:
    - port: 80
      targetPort: 8080

Apply:

Bash
kubectl apply -f demo-go.yaml

The sidecar.opentelemetry.io/inject: "true" annotation tells the Collector DaemonSet to auto‑inject the OTel agent as a sidecar (if you enable that feature). For this guide we rely on the app’s native OTLP exporter, so the sidecar isn’t strictly required.

---

Configure tracing to Jaeger

Jaeger runs as a standalone deployment in the same namespace.

Bash
helm repo add jaegertracing https://jaegertracing.github.io/helm-charts
helm repo update
helm install jaeger jaegertracing/jaeger \
  -n observability \
  --set collector.enabled=true \
  --set query.ingress.enabled=true \
  --set query.ingress.hosts[0]=jaeger.example.com \
  --version 0.73.0

The Collector’s jaeger exporter (see values.yaml) forwards spans to jaeger-collector:14250. Open https://jaeger.example.com and you’ll see the demo-go service appear after a few seconds.

---

Export metrics to Prometheus

The Collector already has a prometheusremotewrite exporter pointing at the Prometheus server service (prometheus-server). Install Prometheus with the community chart:

Bash
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update
helm install prometheus prometheus-community/kube-prometheus-stack \
  -n monitoring \
  --create-namespace \
  --set prometheus.prometheusSpec.serviceMonitorSelectorNilUsesHelmValues=false \
  --version 55.6.0

Add a ServiceMonitor for the Collector:

YAML
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: otel-collector
  labels:
    release: prometheus
spec:
  selector:
    matchLabels:
      app.kubernetes.io/name: otel-collector
  endpoints:
    - port: otlp-http
      path: /metrics
      interval: 15s

Apply it in the observability namespace. Now Prometheus scrapes the Collector’s internal metrics (e.g., otelcol_exporter_sent_spans_total). You can also create custom Go metrics (see initMeter) and see them in Grafana.

---

Ship logs to Loki

Loki runs as a Helm release:

Bash
helm repo add grafana https://grafana.github.io/helm-charts
helm repo update
helm install loki grafana/loki-stack \
  -n logging \
  --create-namespace \
  --set loki.service.type=ClusterIP \
  --version 2.9.5

Our Collector’s loki exporter pushes logs received over OTLP. To make the Go app emit logs in the OTel format, add a simple logger:

Go
import (
	"go.opentelemetry.io/otel/sdk/log"
	otellog "go.opentelemetry.io/otel/exporters/otlp/otlpgrpc"
)

func initLogger() func(context.Context) error {
	ctx := context.Background()
	exp, _ := otellog.New(ctx,
		otellog.WithEndpoint(os.Getenv("OTEL_EXPORTER_OTLP_ENDPOINT")),
		otellog.WithInsecure(),
	)
	provider := log.NewLoggerProvider(
		log.WithProcessor(log.NewBatchProcessor(exp)),
	)
	otel.SetLoggerProvider(provider)
	return provider.Shutdown
}

Call defer initLogger()(context.Background()) in main. After a few requests, you’ll see log entries under the Loki data source in Grafana, tagged with service.name="demo-go".

---

Validate end‑to‑end flow

1. Generate traffic

   Bash
   for i in {1..20}; do curl -s http://demo-go/ping; done
   

2. Jaeger – open the UI, select demo-go, verify spans with correct parent/child relationships.

3. Prometheus – query http_requests_total to see the counter increment.

4. Grafana Loki – run {service="demo-go"} |~ "pong" and confirm logs line up with traces.

If any component shows zero data, check the Collector logs (kubectl -n observability logs ds/otel-collector -c otel-collector). Common culprits: wrong OTLP endpoint (must match http://otel-collector.observability.svc:4317) or missing OTEL_EXPORTER_OTLP_INSECURE=true.

---

Production tips & gotchas

Scaling the Collector

For high‑throughput workloads (>10 k spans/sec), switch from DaemonSet to a Deployment with horizontal pod autoscaling:

YAML
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: otel-collector
  namespace: observability
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: otel-collector
  minReplicas: 2
  maxReplicas: 8
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70

---

Wrap‑up

OpenTelemetry gives you a single, vendor‑agnostic data plane. By deploying the Collector as a DaemonSet and wiring Jaeger, Prometheus, and Loki, you get:

• Distributed tracing that correlates across services
• Metrics that survive pod restarts and feed directly into alerting pipelines
• Logs that inherit trace IDs, making root‑cause analysis painless

All the YAML and Go snippets are in the GitHub repo linked below. Clone it, spin up a cluster, and you’ll have a production‑grade observability stack in under 15 minutes.

Next steps – explore OpenTelemetry’s resource detection for Kubernetes (pod name, node name) and add semantic conventions (semconv.SemanticConventions) to enrich your data.

Happy tracing!

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GitHub repo: https://github.com/rubel/otel-k8s-demo

Further reading:

• OpenTelemetry Collector documentation – https://opentelemetry.io/docs/collector/
• Jaeger tracing guide – https://www.jaegertracing.io/docs/
• Prometheus remote write – https://prometheus.io/docs/prometheus/latest/configuration/configuration/#remote_write

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Quick reference cheat sheet