Performance Engineering using Go

In this series, we will know How to do performance Engineering using Go and will see the below 3 types in the series

1. Check for excessive use of resources: If the goroutine is spending a lot of time performing the string formatting operations, it's possible that it's using up too much CPU or memory. You can use Go's built-in profiling tools, such as pprof, to identify where your program is spending most of its time and optimize it accordingly.

2. Check for race conditions: Since goroutines run concurrently, it's possible that multiple goroutines are accessing the same data structures or variables at the same time, causing race conditions. You can use Go's built-in race detector by running your program with the -race flag to identify any race conditions.

3. Check for deadlocks: Goroutines can also deadlock if they are waiting on each other to complete or waiting on a resource that is not available. You can use the pprof tool in combination with the net/http package to generate a deadlock graph and analyze it to identify the cause of the deadlock.

Check for excessive use of resources :

1) pprof is a command-line tool that generates a visualization of a program's CPU usage, memory usage, and contention, and provides insights into where the program is spending most of its time

2) Use pprof to generate a deadlock graph and analyze it to identify the cause of the deadlock.

When optimizing the performance of a Go program, it can be challenging to identify the bottlenecks and areas where the program is spending most of its time.

pprof is a performance profiling tool that helps identify the hotspots in a program, where it's spending most of its CPU time and memory usage.

To use pprof, you first need to import the net/http/pprof package into your code and start an HTTP server to collect the profiling data. You can then use the go tool pprof command to generate an interactive visualization of the profiling data, which can be analyzed to identify the areas where the program is spending most of its time.

The result of using pprof is a better understanding of the performance of your Go program, and insights into how to optimize it. You can use the profiling data to identify bottlenecks and make targeted optimizations to improve the program's

package main

import (
    "math/rand"
    "os"
    "runtime/pprof"
    "sort"
)

func main() {
    // Start a CPU profiling session
    f, err := os.Create("cpu.prof")
    if err != nil {
        panic(err)
    }
    defer f.Close()
    if err := pprof.StartCPUProfile(f); err != nil {
        panic(err)
    }
    defer pprof.StopCPUProfile()

    nums := make([]int, 100000)
    for i := range nums {
        nums[i] = rand.Intn(1000)
    }
    sort.Ints(nums)
}

And run the code as mentioned in below

\>> $ go tool pprof cpu.prof

This will open an interactive shell where we can analyze the profile. The top command shows us the functions that consume the most CPU time:

In summary, pprof is a powerful tool for identifying performance issues in Go programs, and using it can lead to significant performance improvements