How do you benchmark concurrent code with testing.B and what insights does it provide?

Serial benchmarks (for i := 0; i < b.N; i++) measure single-goroutine throughput. Parallel benchmarks reveal lock contention, cache coherence issues, and true concurrent throughput — critical for shared data structures and handlers.

// Serial benchmark: single goroutine throughput
func BenchmarkMapGet(b *testing.B) {
    m := map[string]int{"key": 1}
    b.ResetTimer()
    for i := 0; i < b.N; i++ {
        _ = m["key"]
    }
}

// Parallel benchmark: concurrent throughput + contention
func BenchmarkSyncMapGet(b *testing.B) {
    var m sync.Map
    m.Store("key", 1)

    b.ResetTimer()
    b.RunParallel(func(pb *testing.PB) {
        for pb.Next() { // pb.Next() is goroutine-safe, replaces i < b.N
            m.Load("key")
        }
    })
}

// Compare mutex-protected map vs sync.Map under contention
type MutexMap struct {
    mu sync.RWMutex
    m  map[string]int
}

func BenchmarkMutexMapVsSyncMap(b *testing.B) {
    b.Run("mutex-map", func(b *testing.B) {
        mm := &MutexMap{m: map[string]int{"k": 1}}
        b.RunParallel(func(pb *testing.PB) {
            for pb.Next() {
                mm.mu.RLock()
                _ = mm.m["k"]
                mm.mu.RUnlock()
            }
        })
    })

    b.Run("sync-map", func(b *testing.B) {
        var sm sync.Map
        sm.Store("k", 1)
        b.RunParallel(func(pb *testing.PB) {
            for pb.Next() { sm.Load("k") }
        })
    })
}

// Run with multiple parallelism levels:
// go test -bench=BenchmarkMutexMapVsSyncMap -cpu=1,4,8,16
// -cpu controls GOMAXPROCS; shows how performance scales with CPUs

// Custom metric reporting
b.SetParallelism(10) // GOMAXPROCS * 10 goroutines
b.ReportMetric(float64(b.N)/b.Elapsed().Seconds(), "rps")
b.ReportMetric(float64(contention)/float64(b.N), "contentions/op")

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