Exercise 6: Performance Optimization in Go

Why Optimize Performance?

Optimizing performance helps reduce resource consumption, improve execution speed, and ensure better scalability.

Performance Analysis with `pprof`

Go provides the `pprof` tool to analyze a program's performance.

package main

import (
    "fmt"
    "os"
    "runtime/pprof"
)

func compute() {
    for i := 0; i < 1000000; i++ {
        _ = i * i
    }
}

func main() {
    f, _ := os.Create("cpu.prof")
    pprof.StartCPUProfile(f)
    defer pprof.StopCPUProfile()

    compute()
    fmt.Println("Profiling complete")
}

Optimizing Memory Usage

Use `sync.Pool` to reduce excessive memory allocations.

package main

import (
    "fmt"
    "sync"
)

var bufferPool = sync.Pool{
    New: func() interface{} { return make([]byte, 1024) },
}

func main() {
    buffer := bufferPool.Get().([]byte)
    fmt.Println("Optimized memory reuse")
    bufferPool.Put(buffer)
}

Exercise Instructions

• Create a file named performance.go.
• Add a CPU-intensive function.
• Analyze performance using `pprof`.
• Optimize memory usage with `sync.Pool`.

Exercise Solution

package main

import (
    "fmt"
    "os"
    "runtime/pprof"
    "sync"
)

var bufferPool = sync.Pool{
    New: func() interface{} { return make([]byte, 1024) },
}

func compute() {
    for i := 0; i < 5000000; i++ {
        _ = i * i
    }
}

func main() {
    f, _ := os.Create("cpu.prof")
    pprof.StartCPUProfile(f)
    defer pprof.StopCPUProfile()
    
    compute()
    fmt.Println("Profiling complete")
    
    buffer := bufferPool.Get().([]byte)
    fmt.Println("Optimizing memory usage...")
    bufferPool.Put(buffer)
}

Best Practices and Common Errors

• ✅ Always profile a program before optimizing.
• ✅ Use `sync.Pool` to manage memory efficiently.
• ✅ Identify CPU-intensive functions before modifying them.
• ⚠️ Avoid excessive memory allocations.