aeondave/golang-performance

Go Performance

This skill is about measurement-first optimization in Go.

When to activate

Use this skill when you need to:

• Confirm a performance regression (latency/throughput/CPU/memory)
• Identify hot paths with pprof (CPU / heap / mutex / block)
• Reduce allocations and GC pressure in a measured hotspot
• Fix contention (mutex, scheduler, channel backpressure)
• Validate improvements with benchmarks and repeatable runs

If you need general idioms and patterns (not measurement), use golang-patterns.

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Rules of engagement

• Profile before optimizing. A fast guess beats a slow change.
• Change one thing at a time. Measure after each change.
• Keep a baseline. Every claim should have “before vs after”.
• Don’t optimize the cold path. Make the hot path boring.

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Outcome expectations

• Performance claims are backed by reproducible before/after measurements.
• Profile type selection matches the observed symptom.
• Optimizations are incremental, attributable, and regression-resistant.

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Workflow

1. Make it measurable

   • Add a benchmark (or a reproducible load test) for the suspected hotspot.
   • Run multiple iterations; record mean + variance.

2. Capture evidence

   • CPU profile for time
   • Heap/allocs profile for memory
   • Mutex/block profiles for contention
   • Trace when the scheduler / GC behavior matters

3. Analyze before changing code

   • Identify top offenders (top, top -cum)
   • Inspect annotated source (list)
   • Confirm whether you are bound by CPU, allocations, syscalls, or contention

4. Apply targeted fixes

   • Allocation and GC: reduce allocations, reuse buffers, avoid retaining large backing arrays
   • Data layout: improve locality, avoid interface boxing in hot loops
   • Concurrency: reduce contention, bound goroutines, add backpressure

5. Verify and document

   • Re-run the benchmark/profile
   • Ensure correctness isn’t traded away
   • Record the change and its measured impact

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Symptom to first profile mapping

• High CPU -> CPU profile
• Memory growth -> heap profile (compare snapshots)
• High allocation churn / GC pressure -> allocs profile
• Latency spikes without CPU spike -> block profile
• Lock contention suspicion -> mutex profile
• Scheduler/pathological latency behavior -> runtime trace

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Safety note: exposing pprof

net/http/pprof endpoints can leak sensitive runtime data. Prefer:

• bind to localhost
• protect with auth / firewall
• enable only in dev / controlled environments

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Resources

Load these references on demand:

• references/profiling.md — pprof + trace collection and analysis commands
• references/benchmarks.md — stable benchmarks, -benchmem, benchstat, hygiene
• references/allocations-gc.md — allocation patterns, slice retention, sync.Pool guidance
• references/contention.md — mutex/block profiles, contention patterns, backpressure