Which Gin Observability Method Wins? Manual, Compile‑time, or eBPF?

In the cloud‑native era, Golang has become the preferred language and Gin is the most popular Go web framework. This article introduces three official observability solutions for Gin—manual instrumentation, compile‑time injection, and eBPF automatic tracing—provides step‑by‑step implementations, compares their integration cost, compatibility, completeness, security, performance and maintenance, and recommends the compile‑time injection approach as the most balanced solution.

Preparation

1. Write a simple Gin application:

package main

import (
    "io"
    "log"
    "net/http"
    "time"

    "github.com/gin-gonic/gin"
)

func main() {
    r := gin.Default()
    r.GET("/hello-gin", func(c *gin.Context) {
        c.String(http.StatusOK, "hello
")
    })
    go func() { _ = r.Run() }()
    time.Sleep(5 * time.Second)
    for {
        resp, err := http.Get("http://localhost:8080/hello-gin")
        if err != nil { log.Fatal(err) }
        body, err := io.ReadAll(resp.Body)
        if err != nil { log.Fatal(err) }
        log.Printf("Body: %s
", string(body))
        _ = resp.Body.Close()
        time.Sleep(5 * time.Second)
    }
}

2. Start OpenTelemetry dependencies (Collector, Jaeger, Prometheus, etc.) as described in the documentation.

Manual Instrumentation

Manual instrumentation uses Gin middleware to create spans for each request. The following code adds OpenTelemetry resources, exporter, and middleware:

const (
    SERVICE_NAME       = ""
    SERVICE_VERSION    = ""
    DEPLOY_ENVIRONMENT = ""
    HTTP_ENDPOINT      = ""
    HTTP_URL_PATH      = ""
)

func newResource(ctx context.Context) *resource.Resource {
    hostName, _ := os.Hostname()
    r, err := resource.New(
        ctx,
        resource.WithFromEnv(),
        resource.WithProcess(),
        resource.WithTelemetrySDK(),
        resource.WithHost(),
        resource.WithAttributes(
            semconv.ServiceNameKey.String(SERVICE_NAME),
            semconv.ServiceVersionKey.String(SERVICE_VERSION),
            semconv.DeploymentEnvironmentKey.String(DEPLOY_ENVIRONMENT),
            semconv.HostNameKey.String(hostName),
        ),
    )
    if err != nil { log.Fatalf("%s: %v", "Failed to create OpenTelemetry resource", err) }
    return r
}

func newHTTPExporterAndSpanProcessor(ctx context.Context) (*otlptrace.Exporter, sdktrace.SpanProcessor) {
    traceExporter, err := otlptrace.New(ctx, otlptracehttp.NewClient(
        otlptracehttp.WithEndpoint(HTTP_ENDPOINT),
        otlptracehttp.WithURLPath(HTTP_URL_PATH),
        otlptracehttp.WithInsecure(),
        otlptracehttp.WithCompression(1),
    ))
    if err != nil { log.Fatalf("%s: %v", "Failed to create the OpenTelemetry trace exporter", err) }
    batchSpanProcessor := sdktrace.NewBatchSpanProcessor(traceExporter)
    return traceExporter, batchSpanProcessor
}

func InitOpenTelemetry() func() {
    ctx := context.Background()
    traceExporter, batchSpanProcessor := newHTTPExporterAndSpanProcessor(ctx)
    otelResource := newResource(ctx)
    traceProvider := sdktrace.NewTracerProvider(
        sdktrace.WithSampler(sdktrace.AlwaysSample()),
        sdktrace.WithResource(otelResource),
        sdktrace.WithSpanProcessor(batchSpanProcessor),
    )
    otel.SetTracerProvider(traceProvider)
    otel.SetTextMapPropagator(propagation.NewCompositeTextMapPropagator(propagation.TraceContext{}, propagation.Baggage{}))
    return func() {
        cxt, cancel := context.WithTimeout(ctx, time.Second)
        defer cancel()
        if err := traceExporter.Shutdown(cxt); err != nil { otel.Handle(err) }
    }
}

func main() {
    r := gin.Default()
    tp, err := InitOpenTelemetry()
    if err != nil { log.Fatal(err) }
    defer func() { if err := tp.Shutdown(context.Background()); err != nil { log.Printf("Error shutting down tracer provider: %v", err) } }()
    r.Use(otelgin.Middleware("my-server"))
    r.GET("/hello-gin", func(c *gin.Context) { c.String(http.StatusOK, "hello
") })
    // start server ...
}

Manual instrumentation offers high flexibility but requires significant code changes and only captures the Gin service itself.

Compile‑time Injection (Zero‑Code Change)

The compile‑time approach uses Alibaba's Golang Agent to automatically inject OpenTelemetry code during build. Steps:

Download the Golang Agent binary from the project homepage.

Compile the application with the agent instead of go build:

otel-linux-amd64 go build .

Configure the export endpoint and run the instrumented binary.

The resulting binary reports full trace data and runtime metrics without modifying source code.

eBPF Automatic Tracing

eBPF attaches a privileged sidecar container to the application’s process namespace, automatically capturing traces and sending them to Jaeger. Example Kubernetes deployment:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: emoji
  namespace: emojivoto
spec:
  replicas: 1
  selector:
    matchLabels:
      app: emoji-svc
  template:
    metadata:
      labels:
        app: emoji-svc
    spec:
      containers:
      - name: emoji-svc
        image: registry.cn-hangzhou.aliyuncs.com/private-mesh/ginotel:latest
        env:
        - name: HTTP
          value: '8080'
      - name: emojivoto-emoji-instrumentation
        image: ghcr.io/open-telemetry/opentelemetry-go-instrumentation/autoinstrumentation-go:v0.19.0-alpha
        env:
        - name: OTEL_GO_AUTO_TARGET_EXE
          value: /usr/local/bin/app
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: 'http://jaeger.default.svc:4318'
        - name: OTEL_SERVICE_NAME
          value: emojivoto-emoji
        securityContext:
          privileged: true
          runAsUser: 0
      shareProcessNamespace: true

eBPF provides the lowest integration cost but has compatibility constraints (sensitive to Go version, limited HTTP headers, high kernel version requirement) and may impact performance.

Comparison Summary

Manual instrumentation offers the highest flexibility but incurs the greatest integration and maintenance effort. Compile‑time injection balances low overhead with comprehensive tracing and metrics, while eBPF delivers zero‑code changes at the expense of compatibility, security, and performance.

Overall, the compile‑time injection method is recommended as the most suitable Gin observability solution for most cloud‑native deployments.