What is the Go optimisation workflow? How do you go from a performance problem to a fix?

Premature optimisation is wasteful; uninformed optimisation is harmful. The Go ecosystem provides a disciplined, measurement-driven workflow: profile first, identify the actual bottleneck, optimise, verify the improvement, and repeat.

// Step 1: establish baseline with benchmarks
// go test -bench=BenchmarkFoo -count=5 -benchmem > before.txt

// Step 2: collect a CPU profile
// go test -bench=BenchmarkFoo -cpuprofile=cpu.out
// go tool pprof cpu.out
// (pprof) top10         — hottest functions
// (pprof) web           — flame graph (requires graphviz)
// (pprof) list hotFunc  — annotated source

// Step 3: collect a memory profile
// go test -bench=BenchmarkFoo -memprofile=mem.out
// go tool pprof mem.out
// (pprof) top10 -cum   — allocation hotspots

// Step 4: check escape analysis — are expected stack allocs escaping?
// go build -gcflags="-m -m" ./...

// Step 5: common optimisations to check:
// - Replace []byte string(b) conversions in hot paths
// - Pre-allocate slices with known capacity: make([]T, 0, n)
// - Use sync.Pool for frequently allocated/freed objects
// - Replace interface{} parameters with generics (Go 1.18+)
// - Replace reflect with code generation (go generate)
// - Use buffered I/O (bufio.Writer) instead of unbuffered

// Step 6: verify improvement
// go test -bench=BenchmarkFoo -count=5 -benchmem > after.txt
// benchstat before.txt after.txt
// Outputs: delta, p-value, whether improvement is statistically significant

// Step 7: run with -race to ensure no correctness regression
// go test -race ./...

The golden rule: measure first. Intuition about where time is spent is usually wrong. The pprof CPU profile shows the actual hot path. A 10% improvement in a function that takes 1% of total time is invisible; a 10% improvement in a function that takes 80% is significant. Use benchstat to confirm improvements are statistically significant and not just noise.

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