golang-benchmark

$28

func BenchmarkParse(b *testing.B) {

    data := loadFixture("large.json") // setup — excluded from timing

    for b.Loop() {

        Parse(data)  // compiler cannot eliminate this call

    }

}

Legacy b.N loops still compile and are fine to keep when preserving existing benchmarks or supporting Go <1.24. They are easier to get wrong: setup may need b.ResetTimer(), and results may need a sink if the compiler can eliminate the work. Go 1.26 fixed an earlier b.Loop() inlining limitation — benchmarks on 1.24–1.25 already benefit from b.Loop() but may miss inlining optimizations that 1.26 delivers.

Memory tracking

func BenchmarkAlloc(b *testing.B) {

    b.ReportAllocs() // or run with -benchmem flag

    var sink []byte

    for b.Loop() {

        sink = make([]byte, 1024)

    }

    _ = sink

}

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

b.ReportMetric(float64(totalBytes)/b.Elapsed().Seconds(), "bytes/s") // b.Elapsed() is only valid inside b.Loop()

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.

Documenting Results in Commits

Paste benchstat output in the commit body when the change has a measurable performance impact. This documents why an optimization was made, prevents future readers from reverting it, and lets reviewers verify the claim without re-running benchmarks.

Commit format:

perf(parser): reduce Parse allocations 50% with sync.Pool

Replace per-call []byte allocation with a pooled buffer.

goos: linux / goarch: amd64 / cpu: AMD Ryzen 9 5950X

          │    old     │              new               │

          │  sec/op    │  sec/op     vs base            │

Parse-32    4.592µ ± 2%  3.041µ ± 1%  -33.78% (p=0.000 n=10)

          │   old    │             new              │

          │   B/op   │   B/op     vs base           │

Parse-32   1.024Ki ± 0%  0.512Ki ± 0%  -50.00% (p=0.000 n=10)

          │ old  │            new             │

          │ allocs/op │ allocs/op  vs base    │

Parse-32   12.00 ± 0%   6.000 ± 0%  -50.00% (p=0.000 n=10)

Rules:

• Only include benchmarks directly affected by the change — strip unrelated rows

• Never paste results with ~ (no statistical significance) — the improvement cannot be claimed

• Include the hardware context line (goos/goarch/cpu) so results are reproducible

• Use perf(scope): commit type for performance-only changes

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