Tips to become a Python pro

Harsha Vardhan

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Better Programming

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8 min read

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Nov 5, 2021

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Whenever we work with Python applications, profiling is necessary as it increases the application’s performance — quicker response time for the user and fast processing of your inputs. Profiling helps optimize the code, i.e., replace statements and functions that consume too many CPU resources with those that consume lesser resources. It is necessary to optimize the utilization of the cloud environment because everything usually works on a pay-per-use basis in a cloud environment. So, if a lot of resources are used by the program or application, it will cost the organization a lot.

1. Profiling for Proper Utilization of CPU Resources

In Python, we use a profiler to find out a program’s statistics, such as time taken to execute the function, the number of times a function is called, how much memory has been used, and so on. Python has inbuilt profilers such as cProfile, line_profiler, and memory_profiler, which can give you a lot of stats about a program.

When we run Python in the production environment, there might occur a situation in which many CPU resources are used by a single program or code, leaving very little memory for the execution of other programs. This results in slower responses and denial of service — when an application or server is not able to process user requests.

2. Using a Continuous Profiler

First: what is continuous profiling? We capture line-level performance data from a production environment and deliver it to developers and other teams for speedy analysis in continuous profiling. If this system is in place, we will be able to gain a line-by-line insight of the code’s performance, such as memory and CPU usage; i.e., the depletion of some important finite resources, which may result in bottlenecks when depleted. There are many continuous profilers out there, but for this article, we’ll look at one.

gProfiler is an open-source continuous profiling tool that is far superior to the profiling tools available in Python. It can track the statistics of a wide range of languages, including Python, Java, and Go. It is plug-and-play, so it provides seamless…