How do you find and fix memory allocation hotspots in a Go service using profiling?

Memory allocation hotspots cause GC pressure, latency spikes, and higher CPU usage. The workflow: benchmark to detect allocations, profile to find the source, fix (pre-allocate, use sync.Pool, reduce interface boxing), benchmark again to verify improvement.

// Step 1: identify hotspots with -benchmem
// go test -bench=BenchmarkProcessRequests -benchmem -memprofile=mem.out
// go tool pprof mem.out
// > top10 -cum
// > list processRequest

// Step 2: fix common allocation patterns

// PATTERN 1: pre-allocate slices to known capacity
// Allocates N times as the slice grows:
func collectIDs(users []User) []int {
    var ids []int
    for _, u := range users { ids = append(ids, u.ID) }
    return ids
}
// Zero allocations:
func collectIDsFast(users []User) []int {
    ids := make([]int, 0, len(users)) // pre-allocate exact capacity
    for _, u := range users { ids = append(ids, u.ID) }
    return ids
}

// PATTERN 2: sync.Pool for frequently allocated/freed objects
var bufPool = sync.Pool{
    New: func() any { return &bytes.Buffer{} },
}

func encodeResponse(v any) ([]byte, error) {
    buf := bufPool.Get().(*bytes.Buffer)
    buf.Reset()
    defer bufPool.Put(buf)
    if err := json.NewEncoder(buf).Encode(v); err != nil {
        return nil, err
    }
    return buf.Bytes(), nil
}

// PATTERN 3: avoid interface boxing of small values
// This allocates (int escapes to heap when stored as interface):
func logValue(v interface{}) { fmt.Println(v) }
logValue(42) // 42 allocated on heap

// Use type-specific overloads or generics instead
func logInt(v int) { fmt.Println(v) } // no allocation

// PATTERN 4: strings.Builder instead of string concatenation
// Step 3: verify with benchmark comparison
// go test -bench=BenchmarkCollectIDs -benchmem -count=5 > after.txt
// benchstat before.txt after.txt

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