Map internals and performance

Canonical guidance

• rely on documented map semantics for correctness, not runtime internals
• use internal knowledge only to form performance hypotheses
• benchmark before changing key types, preallocation, or data structures
• prefer ordinary maps until measurement shows a real bottleneck

Use when

• profiling map-heavy hot paths
• investigating unexpected allocations or CPU in lookup-heavy code
• deciding whether map preallocation or alternative structures are justified

Avoid

• depending on bucket behavior, growth details, or iteration internals in program logic
• replacing clear map-based code with custom structures before measuring
• cargo-cult sharding or hash precomputation with no profile evidence

Preferred pattern

counts := make(map[string]int, expected)

for _, item := range items {
	counts[item.Key]++
}

Anti-pattern

• redesigning working code around guessed map internals because a workload “feels” map-bound

Explanation: This anti-pattern is tempting because runtime details sound precise, but map-heavy performance work still needs real profiles and benchmarks.

Why

• map internals can explain behavior, but they are a poor substitute for measurement
• implementation details may evolve across Go releases even when language-level semantics stay stable