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 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
- 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
1---2name: golang-performance3description: 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).4license: MIT5---67**Persona:** You are a Go performance engineer. You never optimize without profiling first — measure, hypothesize, change one thing, re-measure.89**Thinking mode:** Use `ultrathink` for performance optimization. Shallow analysis misidentifies bottlenecks — deep reasoning ensures the right optimization is applied to the right problem.1011**Modes:**1213- **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.14- **Review mode (hot path)** — focused analysis of a single function or tight loop identified by the caller. Work sequentially; one sub-agent is sufficient.15- **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.1617# Go Performance Optimization1819## Core Philosophy20211. **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)222. **Allocation reduction yields the biggest ROI** — Go's GC is fast but not free. Reducing allocations per request often matters more than micro-optimizing CPU233. **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" optimization2425## Rule Out External Bottlenecks First2627Before 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.2829**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 slow3031**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)).3233## Iterative Optimization Methodology3435### The cycle: Define Goals → Benchmark → Diagnose → Improve → Benchmark36371. **Define your metric** — latency, throughput, memory, or CPU? Without a target, optimizations are random382. **Write an atomic benchmark** — isolate one function per benchmark to avoid result contamination (→ See `samber/cc-skills-golang@golang-benchmark` skill)393. **Measure baseline** — `go test -bench=BenchmarkMyFunc -benchmem -count=6 ./pkg/... | tee /tmp/report-1.txt`404. **Diagnose** — use the **Diagnose** lines in each deep-dive section to pick the right tool415. **Improve** — apply ONE optimization at a time with an explanatory comment426. **Compare** — `benchstat /tmp/report-1.txt /tmp/report-2.txt` to confirm statistical significance437. **Repeat** — increment report number, tackle next bottleneck4445Refer to library documentation for known patterns before inventing custom solutions. Keep all `/tmp/report-*.txt` files as an audit trail.4647## Decision Tree: Where Is Time Spent?4849| Bottleneck | Signal (from pprof) | Action |50| --- | --- | --- |51| Too many allocations | `alloc_objects` high in heap profile | [Memory optimization](references/memory.md) |52| CPU-bound hot loop | function dominates CPU profile | [CPU optimization](references/cpu.md) |53| GC pauses / OOM | high GC%, container limits | [Runtime tuning](references/runtime.md) |54| Network / I/O latency | goroutines blocked on I/O | [I/O & networking](references/io-networking.md) |55| Repeated expensive work | same computation/fetch multiple times | [Caching patterns](references/caching.md) |56| Wrong algorithm | O(n²) where O(n) exists | [Algorithmic complexity](references/caching.md#algorithmic-complexity) |57| Lock contention | mutex/block profile hot | → See `samber/cc-skills-golang@golang-concurrency` skill |58| Slow queries | DB time dominates traces | → See `samber/cc-skills-golang@golang-database` skill |5960## Common Mistakes6162| Mistake | Fix |63| --- | --- |64| Optimizing without profiling | Profile with pprof first — intuition is wrong ~80% of the time |65| Default `http.Client` without Transport | `MaxIdleConnsPerHost` defaults to 2; set to match your concurrency level |66| Logging in hot loops | Log calls prevent inlining and allocate even when the level is disabled. Use `slog.LogAttrs` |67| `panic`/`recover` as control flow | panic allocates a stack trace and unwinds the stack; use error returns |68| `unsafe` without benchmark proof | Only justified when profiling shows >10% improvement in a verified hot path |69| No GC tuning in containers | Set `GOMEMLIMIT` to 80-90% of container memory to prevent OOM kills |70| `reflect.DeepEqual` in production | 50-200x slower than typed comparison; use `slices.Equal`, `maps.Equal`, `bytes.Equal` |7172## Deep Dives7374- [Memory Optimization](references/memory.md) — allocation patterns, backing array leaks, sync.Pool, struct alignment75- [CPU Optimization](references/cpu.md) — inlining, cache locality, false sharing, ILP, reflection avoidance76- [I/O & Networking](references/io-networking.md) — HTTP transport config, streaming, JSON performance, cgo, batch operations77- [Runtime Tuning](references/runtime.md) — GOGC, GOMEMLIMIT, GC diagnostics, GOMAXPROCS, PGO78- [Caching Patterns](references/caching.md) — algorithmic complexity, compiled patterns, singleflight, work avoidance79- [Production Observability](references/observability.md) — Prometheus metrics, PromQL queries, continuous profiling, alerting rules8081## CI Regression Detection8283Automate benchmark comparison in CI to catch regressions before they reach production. → See `samber/cc-skills-golang@golang-benchmark` skill for `benchdiff` and `cob` setup.8485## Cross-References8687- → See `samber/cc-skills-golang@golang-benchmark` skill for benchmarking methodology, `benchstat`, and `b.Loop()` (Go 1.24+)88- → See `samber/cc-skills-golang@golang-troubleshooting` skill for pprof workflow, escape analysis diagnostics, and performance debugging89- → See `samber/cc-skills-golang@golang-data-structures` skill for slice/map preallocation and `strings.Builder`90- → See `samber/cc-skills-golang@golang-concurrency` skill for worker pools, `sync.Pool` API, goroutine lifecycle, and lock contention91- → See `samber/cc-skills-golang@golang-safety` skill for defer in loops, slice backing array aliasing92- → See `samber/cc-skills-golang@golang-database` skill for connection pool tuning and batch processing93- → See `samber/cc-skills-golang@golang-observability` skill for continuous profiling in production
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