Performance Optimization

In this lesson, explore performance optimization through one small Go program, and connect its output to the language rules behind it.

Optimize against a measured goal: response time, allocations, throughput or CPU usage. Start with a baseline and use benchmarks and profiles to locate expensive work.

The example preallocates a slice when its size is known. It shows a useful pattern, not a claim of universal speedup. Prefer clear correct code unless measurements justify extra complexity.

Follow this diagram before executing the example. Check where a value or decision changes what runs next.

This example is a complete program. Save it as main.go or paste it into the Go Playground.

Go code

package main

import "fmt"

func squares(n int) []int {
	result := make([]int, 0, n)
	for i := 1; i <= n; i++ {
		result = append(result, i*i)
	}
	return result
}
func main() { fmt.Println(squares(5)) }

Output

[1 4 9 16 25]

Capacity is reserved up front, then values are appended. The final slice is [1 4 9 16 25]. Compare allocations in a benchmark before deciding this optimization matters.

• Do not assume microbenchmarks predict application-level behavior.
• Memory preallocation trades estimated capacity for fewer growth allocations.

Compare squares with a version that starts with a nil slice using go test -benchmem.

Tip: Edit the example above, predict the output, and run the modified code to test your reasoning.

You have practiced Performance Optimization in a small, self-contained program. The important habit is to predict what runs, inspect the actual output and distinguish the language rule from the particular example.

Before continuing, make sure you can explain the program to someone who has not seen it, including the line that changes its result.