Skillshub golang-performance

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 golang-benchmark skill) or debugging workflow (see golang-troubleshooting skill).

install

source · Clone the upstream repo

git clone https://github.com/ComeOnOliver/skillshub

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/ComeOnOliver/skillshub "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/Harmeet10000/skills/golang-performance" ~/.claude/skills/comeonoliver-skillshub-golang-performance && rm -rf "$T"

manifest: skills/Harmeet10000/skills/golang-performance/SKILL.md

source content

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

Thinking mode: Use

ultrathink

for performance optimization. Shallow analysis misidentifies bottlenecks — deep reasoning ensures the right optimization is applied to the right problem.

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.

Go Performance Optimization

Core Philosophy

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)
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
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

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

Define your metric — latency, throughput, memory, or CPU? Without a target, optimizations are random
Write an atomic benchmark — isolate one function per benchmark to avoid result contamination (→ See
```
samber/cc-skills-golang@golang-benchmark
```
skill)

Measure baseline —

go test -bench=BenchmarkMyFunc -benchmem -count=6 ./pkg/... | tee /tmp/report-1.txt

Diagnose — use the Diagnose lines in each deep-dive section to pick the right tool
Improve — apply ONE optimization at a time with an explanatory comment

Compare —

benchstat /tmp/report-1.txt /tmp/report-2.txt

to confirm statistical significance

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.

Decision Tree: Where Is Time Spent?

Bottleneck	Signal (from pprof)	Action
Too many allocations	`alloc_objects` high in heap profile	Memory optimization
CPU-bound hot loop	function dominates CPU profile	CPU optimization
GC pauses / OOM	high GC%, container limits	Runtime tuning
Network / I/O latency	goroutines blocked on I/O	I/O & networking
Repeated expensive work	same computation/fetch multiple times	Caching patterns
Wrong algorithm	O(n²) where O(n) exists	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 — allocation patterns, backing array leaks, sync.Pool, struct alignment
CPU Optimization — inlining, cache locality, false sharing, ILP, reflection avoidance
I/O & Networking — HTTP transport config, streaming, JSON performance, cgo, batch operations
Runtime Tuning — GOGC, GOMEMLIMIT, GC diagnostics, GOMAXPROCS, PGO
Caching Patterns — algorithmic complexity, compiled patterns, singleflight, work avoidance
Production Observability — 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