Go Performance Testing

Go Performance Testing

Optimize only after establishing a representative, repeatable measurement and a specific hypothesis.

Core Workflow

1. Define the user-visible metric and representative workload.
2. Stabilize correctness before measuring performance.
3. Write focused benchmarks with realistic inputs and controlled setup.
4. Collect repeated baseline samples in comparable conditions.
5. Use allocations, profiles, and traces to form a causal hypothesis.
6. Make one bounded change.
7. Compare repeated before/after samples statistically.
8. Recheck correctness and the end-to-end workload.

Read Next

Guardrails

• Do not optimize from one benchmark line or an unrepresentative microbenchmark.
• Do not compare runs made under materially different environments.
• Do not treat coverage or race detection as performance evidence.
• Name the profile sample type and distinguish flat from cumulative cost.
• Prefer B.Loop only when the pinned Go version supports it.
• Keep production profile endpoints protected and operationally controlled.
• Do not encode cache-line size, escape output, or inliner budgets as portable facts.
• Do not use sync.Pool as a cache or resource owner.

Source Notes