Memory and CPU Profiling
Explore how to perform memory and CPU profiling in Python to measure where your program spends time and resources. Understand how to use the cProfile module and visualize profiling data with tools like KCacheGrind and RunSnakeRun to identify functions worth optimizing and improve your application's performance.
We'll cover the following...
Profiling a Python program means doing a dynamic analysis that measures the execution time of the program and everything that involves. That means measuring the time spent in each of its functions. This data gives you info about where your program is spending time, and what area might be worth optimizing.
This is a very interesting exercise. Many people focus on local optimization, such as determining, for example, which of the Python 2 functions range or xrange is going to be faster. It turns out that knowing which one is faster may never be an
issue in your program, and that the time gained by using one of the functions above
might not be worth the time you spend researching it or arguing about it with your colleague.
Trying to blindly optimize a program without measuring where it is actually spending its time is a useless exercise. Following ...