Summary - Benchmark

Write a slice benchmark with b.Run and b.ReportAllocs.

Benchmark summary

The flow: write BenchmarkXxx(b *testing.B) → assign the result to sink → keep setup out with b.ResetTimer() → see allocations with -benchmem → split by size with b.Run → compare with -count=10 + benchstat. Take a profile with -cpuprofile if needed.

Remember: a benchmark is interpreted together with profiles and real production measurements, not on its own.

RunParallel

To measure how a concurrently used structure (a cache, a pool) behaves under real load:

func BenchmarkOnbellek(b *testing.B) {
    o := yeniOnbellek()
    b.RunParallel(func(pb *testing.PB) {
        for pb.Next() {
            o.Al("anahtar")
        }
    })
}

Running with -cpu=1,2,4,8 immediately reveals code that doesn't scale (gets slower as cores increase).

Exercise Parallel benchmark

Run, with testing.Benchmark, a benchmark that increments an atomic.Int64 counter inside b.RunParallel with a pb.Next() loop. Print whether the counter is greater than 0.

Show example solution

package main

import (
    "fmt"
    "sync/atomic"
    "testing"
)

func main() {
    var sayac atomic.Int64
    testing.Benchmark(func(b *testing.B) {
        b.RunParallel(func(pb *testing.PB) {
            for pb.Next() {
                sayac.Add(1)
            }
        })
    })
    fmt.Println(sayac.Load() > 0)
}

Test yourself

Which method measures parallel performance?
- b.Loop
- b.Parallel
- b.RunParallel
- b.Run
Show answer

b.RunParallel — RunParallel spreads the work over GOMAXPROCS goroutines, exposing concurrency costs such as lock contention.

Parallel loop

b.RunParallel(func(pb *testing.PB) {
    for pb.____() {
        isle()
    }
})

Show answer

b.RunParallel(func(pb *testing.PB) {
    for pb.Next() {
        isle()
    }
})

Group summary

b.N

Iteration count
b.ResetTimer

Exclude setup
b.Run

Sub-measurement
b.RunParallel

Concurrent load

Next lesson: fmt.Printf

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