What profiling methods have you used in your work?

When working with Go for profiling, I usually use the built-in pprof package, which allows collecting and analyzing profiles of CPU, memory, locks, and goroutines.

Main methods:

• CPU profiling: helps understand which functions take the most processor time.
• Heap profiling: identifies memory leaks and objects occupying a lot of memory.
• Block profiling: analyzes locks and waits.
• Goroutine profiling: shows the state of goroutines, helps identify deadlocks or leaks.

Example of starting a CPU profile:

import (
    "net/http"
    _ "net/http/pprof"
)

func main() {
    go func() {
        log.Println(http.ListenAndServe("localhost:6060", nil))
    }()
    // main code
}

After starting, you can connect to http://localhost:6060/debug/pprof/ and download profiles for analysis using go tool pprof.

I also use external tools, such as perf on Linux for system profiling, and visualizers like pprof.io.