Raks is a simple software package that can capture the time spent in CPU time that is idle and will make you aware of which parts of your program (or code) spend the most time. Raks is a lightweight toolkit that provides tools like time tracking statistics and other basic analytics of your application’s performance. You can run Raks from the command line or install it with Homebrew.
Raks can be run on local machine or on a remote machine and it works in all major operating systems. You have several options to use Raks to measure performance of your applications, as it’s written in C++ so you can do some pretty cool and powerful things.
For example you can use Raks to see where your application spends the most time when you run your application. If you notice that your application uses more time in the startup or logging phases than in the normal loop that is often because of high memory usage. But you can identify the areas where your memory usage is excessive and use Raks to see where it is and where you can optimize the application.
Here is how an example Raks Report looks like.
Raks – Logs Activity with Racket
The Raks Toolbox
Raks is a fairly simple to use software package but it is written in both C++ and Python programming languages. Let’s take a look at the tools and documentation for Raks.
Raks Manual: http://raskoolbox.github.io/raksmanual.html Raks API Reference: http://raskoolbox.github.io/rask-api.html
You’ll first notice that you get an executable RAK_RPS_CONTROL_API.EXE named after the function you installed. The RAK_RPS_CONTROL_API provides a C++ API for retrieving the time spent in CPU time of a process and you might as well use it as a reference to write your own.
Raklog provides a C++ library for logging to standard in Linux OS. If you’d like to learn how to install Raklog on your Linux system it’s an example of how you can find, integrate and set up your own logging backend using C++ and libraries.
Raklog Documentation: http://raskoolbox.github.io/raklog.html
As a general use package, we want to see how your application spends its time so we’ll