Originally published at https://rakyll.org/custom-profiles/.

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Go provides several pprof profiles out of the box to gather profiling data from Go programs.

The builtin profiles provided by the runtime/pprof package:

• profile: CPU profile determines where a program spends its time while actively consuming CPU cycles (as opposed while sleeping or waiting for I/O).
• heap: Heap profile reports the currently live allocations; used to monitor current memory usage or check for memory leaks.
• threadcreate: Thread creation profile reports the sections of the program that lead the creation of new OS threads.
• goroutine: Goroutine profile report the stack traces of all current goroutines.
• block: Block profile show where goroutines block waiting on synchronization primitives (including timer channels). Block profile is not enabled by default; use runtime.SetBlockProfileRate to enable it.
• mutex: Mutex profile reports the lock contentions. When you think your CPU is not fully utilized due to a mutex contention, use this profile. Mutex profile is not enabled by default, see runtime.SetMutexProfileFraction to enable.

Additional to the builtin profiles, runtime/pprof package allows you to export your custom profiles, and instrument your code to record execution stacks that contributes to this profile.

Imagine we have a blob server, and we are writing a Go client for it. And our users want to be able to profile the opened blobs on the client. We can create a profile and record the events of blob opening and closing, so the user can tell how many open blobs they are at any time.

Here is a blobstore package that allows you to open some blobs. We will create a new custom profile and start recording execution stacks that contributes to opening of blobs:

package blobstore

import "runtime/pprof"

var openBlobProfile = pprof.NewProfile("blobstore.Open")

// Open opens a blob, all opened blobs need
// to be closed when no longer in use.
func Open(name string) (*Blob, error) {
	blob := &Blob{name: name}
	// TODO: Initialize the blob...

	openBlobProfile.Add(blob, 2) // add the current execution stack to the profile
	return blob, nil
}

And once users want to close the blob, we need to remove the execution stack associated with the current blob from the profile:

// Close closes the blob and frees the
// underlying resources.
func (b *Blob) Close() error {
	// TODO: Free other resources.
	openBlobProfile.Remove(b)
	return nil
}

And now, from the programs using this package, we should be able to retrieve blobstore.Open profile data and use our daily pprof tools to examine and visualize them.

Let's write a small main program than opens some blobs:

package main

import (
	"fmt"
	"math/rand"
	"net/http"
	_ "net/http/pprof" // as a side effect, registers the pprof endpoints.
	"time"

	"myproject.org/blobstore"
)

func main() {
	for i := 0; i < 1000; i++ {
		name := fmt.Sprintf("task-blob-%d", i)
		go func() {
			b, err := blobstore.Open(name)
			if err != nil {
				// TODO: Handle error.
			}
			defer b.Close()

			// TODO: Perform some work, write to the blob.
		}()
	}
	http.ListenAndServe("localhost:8888", nil)
}

Start the server, then use go tool to read and visualize the profile data:

$ go tool pprof http://localhost:8888/debug/pprof/blobstore.Open
(pprof) top
Showing nodes accounting for 800, 100% of 800 total
      flat  flat%   sum%        cum   cum%
       800   100%   100%        800   100%  main.main.func1 /Users/jbd/src/hello/main.go

You will see that there are 800 open blobs and all openings are coming from main.main.func1. In this small example, there is nothing more to see, but in a complex server you can examine the hottest spots that works with an open blob and find out bottlenecks or leaks.