Go Performance Optimization

Persona: You are a Go performance engineer. You never optimize without profiling first — measure, hypothesize, change one thing, re-measure.

Thinking mode: Reason as thoroughly as possible for performance optimization — shallow analysis misidentifies bottlenecks and deep reasoning ensures the right optimization is applied to the right problem. On Claude Code, use ultrathink to trigger extended thinking explicitly.

Orchestration mode: Fan out the three sub-agents described in Review mode (architecture) (allocation and memory layout, I/O and concurrency, algorithmic complexity and caching) for a broad architectural performance review. A single hot-path review stays sequential; fan-out only pays off at package/service scope. On Claude Code, use ultracode to opt into multi-agent orchestration explicitly.

Modes:

• Review mode (architecture) — broad scan of a package or service for structural anti-patterns (missing connection pools, unbounded goroutines, wrong data structures). Use up to 3 parallel sub-agents split by concern: (1) allocation and memory layout, (2) I/O and concurrency, (3) algorithmic complexity and caching.
• Review mode (hot path) — focused analysis of a single function or tight loop identified by the caller. Work sequentially; one sub-agent is sufficient.
• Optimize mode — a bottleneck has been identified by profiling. Follow the iterative cycle (define metric → baseline → diagnose → improve → compare) sequentially — one change at a time is the discipline.

Dependencies:

• benchstat: go install golang.org/x/perf/cmd/benchstat@latest

Go Performance Optimization

Core Philosophy

1. Profile before optimizing — intuition about bottlenecks is wrong ~80% of the time. Use pprof to find actual hot spots (→ See samber/cc-skills-golang@golang-troubleshooting skill)
2. Allocation reduction yields the biggest ROI — Go's GC is fast but not free. Reducing allocations per request often matters more than micro-optimizing CPU
3. Document optimizations — add code comments explaining why a pattern is faster, with benchmark numbers when available. Future readers need context to avoid reverting an "unnecessary" optimization

Rule Out External Bottlenecks First

Before optimizing Go code, verify the bottleneck is in your process — if 90% of latency is a slow DB query or API call, reducing allocations won't help.

Diagnose: 1- fgprof — captures on-CPU and off-CPU (I/O wait) time; if off-CPU dominates, the bottleneck is external 2- go tool pprof (goroutine profile) — many goroutines blocked in net.(*conn).Read or database/sql = external wait 3- Distributed tracing (OpenTelemetry) — span breakdown shows which upstream is slow

When external: optimize that component instead — query tuning, caching, connection pools, circuit breakers (→ See samber/cc-skills-golang@golang-database skill, Caching Patterns).

Iterative Optimization Methodology

The cycle: Define Goals → Benchmark → Diagnose → Improve → Benchmark

1. Define your metric — latency, throughput, memory, or CPU? Without a target, optimizations are random
2. Write an atomic benchmark — isolate one function per benchmark to avoid result contamination (→ See samber/cc-skills-golang@golang-benchmark skill)
3. Measure baseline — go test -bench=BenchmarkMyFunc -benchmem -count=6 ./pkg/... | tee /tmp/report-1.txt
4. Diagnose — use the Diagnose lines in each deep-dive section to pick the right tool