Persona: You are a Go performance measurement engineer. You never draw conclusions from a single benchmark run — statistical rigor and controlled conditions are prerequisites before any optimization decision.

Thinking mode: Use ultrathink for benchmark analysis, profile interpretation, and performance comparison tasks. Deep reasoning prevents misinterpreting profiling data and ensures statistically sound conclusions.

Go Benchmarking & Performance Measurement

Performance improvement does not exist without measures — if you can measure it, you can improve it.

This skill covers the full measurement workflow: write a benchmark, run it, profile the result, compare before/after with statistical rigor, and track regressions in CI. For optimization patterns to apply after measurement, → See samber/cc-skills-golang@golang-performance skill. For pprof setup on running services, → See samber/cc-skills-golang@golang-troubleshooting skill.

Writing Benchmarks

`b.Loop()` (Go 1.24+) — preferred

b.Loop() prevents the compiler from optimizing away the code under test — without it, the compiler can detect dead results and eliminate them, producing misleadingly fast numbers. It also excludes setup code before the loop from timing automatically.

func BenchmarkParse(b *testing.B) {
    data := loadFixture("large.json") // setup — excluded from timing
    for b.Loop() {
        Parse(data)  // compiler cannot eliminate this call
    }
}

Existing for range b.N benchmarks still work but should migrate to b.Loop() — the old pattern requires manual b.ResetTimer() and a package-level sink variable to prevent dead code elimination.

Memory tracking

func BenchmarkAlloc(b *testing.B) {
    b.ReportAllocs() // or run with -benchmem flag
    for b.Loop() {
        _ = make([]byte, 1024)
    }
}

b.ReportMetric() adds custom metrics (e.g., throughput):

b.ReportMetric(float64(totalBytes)/b.Elapsed().Seconds(), "bytes/s")

Sub-benchmarks and table-driven

func BenchmarkEncode(b *testing.B) {
    for _, size := range []int{64, 256, 4096} {
        b.Run(fmt.Sprintf("size=%d", size), func(b *testing.B) {
            data := make([]byte, size)
            for b.Loop() {
                Encode(data)
            }
        })
    }
}

Running Benchmarks

go test -bench=BenchmarkEncode -benchmem -count=10 ./pkg/... | tee bench.txt

Flag	Purpose
`-bench=.`	Run all benchmarks (regexp filter)
`-benchmem`	Report allocations (B/op, allocs/op)
`-count=10`	Run 10 times for statistical significance
`-benchtime=3s`	Minimum time per benchmark (default 1s)
`-cpu=1,2,4`	Run with different GOMAXPROCS values
`-cpuprofile=cpu.prof`	Write CPU profile
`-memprofile=mem.prof`	Write memory profile
`-trace=trace.out`	Write execution trace

Output format: BenchmarkEncode/size=64-8 5000000 230.5 ns/op 128 B/op 2 allocs/op — the -8 suffix is GOMAXPROCS, ns/op is time per operation, B/op is bytes allocated per op, allocs/op is heap allocation count per op.

Profiling from Benchmarks

Generate profiles directly from benchmark runs — no HTTP server needed:

# CPU profile
go test -bench=BenchmarkParse -cpuprofile=cpu.prof ./pkg/parser
go tool pprof cpu.prof

# Memory profile (alloc_objects shows GC churn, inuse_space shows leaks)
go test -bench=BenchmarkParse -memprofile=mem.prof ./pkg/parser
go tool pprof -alloc_objects mem.prof

# Execution trace
go test -bench=BenchmarkParse -trace=trace.out ./pkg/parser
go tool trace trace.out

For full pprof CLI reference (all commands, non-interactive mode, profile interpretation), see pprof Reference. For execution trace interpretation, see Trace Reference. For statistical comparison, see benchstat Reference.

Reference Files

pprof Reference — Interactive and non-interactive analysis of CPU, memory, and goroutine profiles. Full CLI commands, profile types (CPU vs allocobjects vs inuse_space), web UI navigation, and interpretation patterns. Use this to dive deep into _where time and memory are being spent in your code.
benchstat Reference — Statistical comparison of benchmark runs with rigorous confidence intervals and p-value tests. Covers output reading, filtering old benchmarks, interleaving results for visual clarity, and regression detection. Use this when you need to prove a change made a meaningful performance difference, not just a lucky run.
Trace Reference — Execution tracer for understanding when and why code runs. Visualizes goroutine scheduling, garbage collection phases, network blocking, and custom span annotations. Use this when pprof (which shows where CPU goes) isn't enough — you need to see the timeline of what happened.
Diagnostic Tools — Quick reference for ancillary tools: fieldalignment (struct padding waste), GODEBUG (runtime logging flags), fgprof (frame graph profiles), race detector (concurrency bugs), and others. Use this when you have a specific symptom and need a focused diagnostic — don't reach for pprof if a simpler tool already answers your question.
Compiler Analysis — Low-level compiler optimization insights: escape analysis (when values move to the heap), inlining decisions (which function calls are eliminated), SSA dump (intermediate representation), and assembly output. Use this when benchmarks show allocations you didn't expect, or when you want to verify the compiler did what you intended.
CI Regression Detection — Automated performance regression gating in CI pipelines. Covers three tools (benchdiff for quick PR comparisons, cob for strict threshold-based gating, gobenchdata for long-term trend dashboards), noisy neighbor mitigation strategies (why cloud CI benchmarks vary 5-10% even on quiet machines), and self-hosted runner tuning to make benchmarks reproducible. Use this when you want to ensure pull requests don't silently slow down your codebase — detecting regressions early prevents shipping performance debt.
Investigation Session — Production performance troubleshooting workflow combining Prometheus runtime metrics (heap size, GC frequency, goroutine counts), PromQL queries to correlate metrics with code changes, runtime configuration flags (GODEBUG env vars to enable GC logging), and cost warnings (when you're hitting performance tax). Use this when production benchmarks look good but real traffic behaves differently.
Prometheus Go Metrics Reference — Complete listing of Go runtime metrics actually exposed as Prometheus metrics by prometheus/client_golang. Covers 30 default metrics, 40+ optional metrics (Go 1.17+), process metrics, and common PromQL queries. Distinguishes between runtime/metrics (Go internal data) and Prometheus metrics (what you scrape from /metrics). Use this when setting up monitoring dashboards or writing PromQL queries for production alerts.

Cross-References

→ See samber/cc-skills-golang@golang-performance skill for optimization patterns to apply after measuring ("if X bottleneck, apply Y")
→ See samber/cc-skills-golang@golang-troubleshooting skill for pprof setup on running services (enable, secure, capture), Delve debugger, GODEBUG flags, root cause methodology
→ See samber/cc-skills-golang@golang-observability skill for everyday always-on monitoring, continuous profiling (Pyroscope), distributed tracing (OpenTelemetry)
→ See samber/cc-skills-golang@golang-testing skill for general testing practices
→ See samber/cc-skills@promql-cli skill for querying Prometheus runtime metrics in production to validate benchmark findings

golang-benchmark