golang-performance

---
name: golang-performance
description: "Golang performance optimization patterns and methodology - if X bottleneck, then apply Y. Covers allocation reduction, CPU efficiency, memory layout, GC tuning, pooling, caching, and hot-path optimization. Use when profiling or benchmarks have identified a bottleneck and you need the right optimization pattern to fix it. Also use when performing performance code review to suggest improvements or benchmarks that could help identify quick performance gains. Not for measurement methodology (→ See `samber/cc-skills-golang@golang-benchmark` skill) or debugging workflow (→ See `samber/cc-skills-golang@golang-troubleshooting` skill)."
user-invocable: true
license: MIT
compatibility: Designed for Claude Code, Codex or similar harness, and for projects using Golang.
metadata:
  author: samber
  version: "1.3.2"
  openclaw:
    emoji: "🏎"
    homepage: https://github.com/samber/cc-skills-golang
    requires:
      bins:
        - go
        - benchstat
    install:
      - kind: go
        package: golang.org/x/perf/cmd/benchstat@latest
        bins: [benchstat]
allowed-tools: Read Edit Write Glob Grep Bash(go:*) Bash(golangci-lint:*) Bash(git:*) Agent WebFetch Bash(benchstat:*) Bash(fieldalignment:*) Bash(staticcheck:*) Bash(curl:*) Bash(fgprof:*) Bash(perf:*) WebSearch AskUserQuestion EnterWorktree ExitWorktree
paths:
  - "**/*.go"
---

**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](references/caching.md)).

## 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
5. **Improve** — apply ONE optimization at a time with an explanatory comment
6. **Compare** — `benchstat /tmp/report-1.txt /tmp/report-2.txt` to confirm statistical significance
7. **Commit** — paste the benchstat output in the commit body so reviewers and future readers see the exact improvement; follow the `perf(scope): summary` commit type
8. **Repeat** — increment report number, tackle next bottleneck

Refer to library documentation for known patterns before inventing custom solutions. Keep all `/tmp/report-*.txt` files as an audit trail.

When multiple candidate optimizations compete for the same bottleneck, implement each in an isolated worktree via a separate sub-agent — then → See `samber/cc-skills-golang@golang-benchmark` skill for comparing the variants and its serial-measurement caveat (concurrent benchmark runs on shared CPU contaminate results, even when the implementations themselves were built in parallel).

## Decision Tree: Where Is Time Spent?

| Bottleneck | Signal (from pprof) | Action |
| --- | --- | --- |
| Too many allocations | `alloc_objects` high in heap profile | [Memory optimization](references/memory.md) |
| CPU-bound hot loop | function dominates CPU profile | [CPU optimization](references/cpu.md) |
| GC pauses / OOM | high GC%, container limits | [Runtime tuning](references/runtime.md) |
| Network / I/O latency | goroutines blocked on I/O | [I/O & networking](references/io-networking.md) |
| Repeated expensive work | same computation/fetch multiple times | [Caching patterns](references/caching.md) |
| Wrong algorithm | O(n²) where O(n) exists | [Algorithmic complexity](references/caching.md#algorithmic-complexity) |
| Lock contention | mutex/block profile hot | → See `samber/cc-skills-golang@golang-concurrency` skill |
| Slow queries | DB time dominates traces | → See `samber/cc-skills-golang@golang-database` skill |

## Common Mistakes

| Mistake | Fix |
| --- | --- |
| Optimizing without profiling | Profile with pprof first — intuition is wrong ~80% of the time |
| Default `http.Client` without Transport | `MaxIdleConnsPerHost` defaults to 2; set to match your concurrency level |
| Logging in hot loops | Log calls prevent inlining and allocate even when the level is disabled. Use `slog.LogAttrs` |
| `panic`/`recover` as control flow | panic allocates a stack trace and unwinds the stack; use error returns |
| `unsafe` without benchmark proof | Only justified when profiling shows >10% improvement in a verified hot path |
| No GC tuning in containers | Set `GOMEMLIMIT` to 80-90% of container memory to prevent OOM kills |
| `reflect.DeepEqual` in production | 50-200x slower than typed comparison; use `slices.Equal`, `maps.Equal`, `bytes.Equal` |

## Deep Dives

- [Memory Optimization](references/memory.md) — allocation patterns, backing array leaks, sync.Pool, struct alignment
- [CPU Optimization](references/cpu.md) — inlining, cache locality, false sharing, ILP, reflection avoidance
- [I/O & Networking](references/io-networking.md) — HTTP transport config, streaming, JSON performance, cgo, batch operations
- [Runtime Tuning](references/runtime.md) — GOGC, GOMEMLIMIT, GC diagnostics, GOMAXPROCS, PGO
- [Caching Patterns](references/caching.md) — algorithmic complexity, compiled patterns, singleflight, work avoidance
- [Production Observability](references/observability.md) — Prometheus metrics, PromQL queries, continuous profiling, alerting rules

## CI Regression Detection

Automate benchmark comparison in CI to catch regressions before they reach production. → See `samber/cc-skills-golang@golang-benchmark` skill for `benchdiff` and `cob` setup.

## Cross-References

- → See `samber/cc-skills-golang@golang-benchmark` skill for benchmarking methodology, `benchstat`, and `b.Loop()` (Go 1.24+)
- → See `samber/cc-skills-golang@golang-troubleshooting` skill for pprof workflow, escape analysis diagnostics, and performance debugging
- → See `samber/cc-skills-golang@golang-data-structures` skill for slice/map preallocation and `strings.Builder`
- → See `samber/cc-skills-golang@golang-concurrency` skill for worker pools, `sync.Pool` API, goroutine lifecycle, and lock contention
- → See `samber/cc-skills-golang@golang-safety` skill for defer in loops, slice backing array aliasing
- → See `samber/cc-skills-golang@golang-database` skill for connection pool tuning and batch processing
- → See `samber/cc-skills-golang@golang-observability` skill for continuous profiling in production