How would you profile and optimize a Go service that has high GC pressure?

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Accepted Answer

// Add pprof endpoints to your HTTP server
import _ "net/http/pprof"

// Heap profile: what is allocated and what is keeping objects alive
// go tool pprof http://localhost:6060/debug/pprof/heap

// CPU profile: where is time being spent
// go tool pprof http://localhost:6060/debug/pprof/profile?seconds=30

// Alloc profile: cumulative allocations since startup
// go tool pprof http://localhost:6060/debug/pprof/allocs

// Visualize as flame graph
// go tool pprof -http=:6061 http://localhost:6060/debug/pprof/heap

// Benchmark to measure allocations
func BenchmarkHandler(b *testing.B) {
    b.ReportAllocs()
    for i := 0; i < b.N; i++ {
        _ = processRequest(testPayload)
    }
} sync.Pool: reuse short-lived objects (byte buffers, encoder/decoder instances) across requests Pre-allocate slices: make([]T, 0, knownSize) avoids repeated doublings Avoid fmt.Sprintf in hot paths: use strings.Builder or strconv for string construction Avoid interface{} boxing: passing concrete types to interface{} parameters allocates on heap Flat data structures: avoid deep pointer graphs — GC must trace every pointer Use GODEBUG=gctrace=1 to monitor GC frequency and pause times in production var bufPool = sync.Pool{
    New: func() interface{} {
        return bytes.NewBuffer(make([]byte, 0, 1024))
    },
}

func handleRequest(data []byte) []byte {
    buf := bufPool.Get().(*bytes.Buffer)
    buf.Reset() // always reset before use
    defer bufPool.Put(buf)

// use buf for processing
    buf.Write(data)
    result := make([]byte, buf.Len())
    copy(result, buf.Bytes())
    return result
}

How would you profile and optimize a Go service that has high GC pressure?

Start with pprof heap and CPU profiles to find top allocators, then reduce allocations using sync.Pool, pre-allocated slices, and avoiding interface boxing in hot paths.

How would you profile and optimize a Go service that has high GC pressure?

Start with pprof heap and CPU profiles to find top allocators, then reduce allocations using sync.Pool, pre-allocated slices, and avoiding interface boxing in hot paths.

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