go-performance

name	go-performance
description	Analyze and optimize Go program performance. Use when asked to profile Go code, find performance bottlenecks, analyze memory allocations, detect memory leaks, write benchmarks, or optimize CPU/memory usage.

Go Performance

Workflow

1. Identify profile source (file, URL, or generate from tests/server)
2. Analyze with go tool pprof
3. Report findings with actionable recommendations

Quick Reference

Profile	Test Flag	HTTP Endpoint
CPU	-cpuprofile	/debug/pprof/profile
Heap	-memprofile	/debug/pprof/heap
Goroutine	-	/debug/pprof/goroutine

Key Commands

# Generate from benchmarks
go test -bench=. -cpuprofile cpu.prof -memprofile mem.prof

# Analyze (interactive or one-liner)
go tool pprof -top -cum cpu.prof
go tool pprof -http=:8080 cpu.prof  # web UI with flame graphs

# Memory analysis
go tool pprof -alloc_space -top mem.prof   # bytes allocated
go tool pprof -alloc_objects -top mem.prof # allocation count (GC pressure)

# Compare profiles (find leaks/regressions)
go tool pprof -base old.prof new.prof

# Filter results
go tool pprof -focus='mypackage.*' cpu.prof

Tips

• Write a benchmark first to reproduce/confirm/measure the issue
• Focus on quick wins first: easy fixes with high impact
• If hotspots are in external libraries, still analyze - source is in $GOPATH
• CPU profiles need 30+ seconds for meaningful data
• For memory leaks: compare heap profiles at two points in time
• Use -base flag to find regressions between profiles

Writing benchmarks

Every time we work on some performance issue, we should:

• make sure there is a benchmark
• if there isn't one, we create it, calling the function that we aim to improve

Then, we create a second benchmark, and a new production function with the changes we want.

This way we can quickly run and compare both benchmarks.

Once we're happy with the changes, we replace the old function and old benchmark with the new ones.

Commit messages should always have the benchmark results in them.