How do you use pprof to profile a live Go service in production?

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Accepted Answer

// Register pprof endpoints — import side-effects only
import _ "net/http/pprof"

// Expose on a separate internal port (never the public port)
func startDebugServer() {
    debugMux := http.NewServeMux()
    debugMux.HandleFunc("/debug/pprof/", pprof.Index)
    debugMux.HandleFunc("/debug/pprof/cmdline", pprof.Cmdline)
    debugMux.HandleFunc("/debug/pprof/profile", pprof.Profile)
    debugMux.HandleFunc("/debug/pprof/symbol", pprof.Symbol)
    debugMux.HandleFunc("/debug/pprof/trace", pprof.Trace)

go http.ListenAndServe(":6060", debugMux) // internal only
}

// In Kubernetes: port-forward before profiling
// kubectl port-forward pod/myservice-xyz 6060:6060

// CPU profile (30 seconds)
// go tool pprof http://localhost:6060/debug/pprof/profile?seconds=30

// Heap profile (in-use allocations)
// go tool pprof http://localhost:6060/debug/pprof/heap

// Goroutine profile (detect leaks)
// go tool pprof http://localhost:6060/debug/pprof/goroutine

// Visualize as interactive flame graph
// go tool pprof -http=:8081 profile.pb.gz

// Trace (scheduler, GC, goroutine events)
// curl http://localhost:6060/debug/pprof/trace?seconds=5 > trace.out
// go tool trace trace.out Reproduce the issue under load, then collect a 30-60 second CPU profile Flame graph: wide boxes are hot code — focus on the widest frames Heap in-use profile shows live allocations — heap allocs profile shows cumulative allocations Compare before/after with pprof diff: pprof -base before.pb.gz after.pb.gz Security: never expose pprof endpoints on public-facing ports — use internal port or VPN

How do you use pprof to profile a live Go service in production?

Register pprof HTTP handlers on an internal port, use go tool pprof to collect CPU, heap, and goroutine profiles, and visualize with flame graphs to find performance bottlenecks.

How do you use pprof to profile a live Go service in production?

Register pprof HTTP handlers on an internal port, use go tool pprof to collect CPU, heap, and goroutine profiles, and visualize with flame graphs to find performance bottlenecks.

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