backend-go-performance（后端 Go 性能）

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

1. Profile before optimizing — intuition about bottlenecks is wrong ~80% of the time. Use pprof to find actual hot spots (→ See jimmy-skills@backend-go-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 jimmy-skills@backend-go-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 jimmy-skills@backend-go-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. 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?

Common Mistakes

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, LFU vs LRU
• Lock-Free Patterns — atomic ops, atomic.Value, channel async, lazy allocation, batch counters
• 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 jimmy-skills@backend-go-benchmark skill for benchdiff and cob setup.

Cross-References

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