{"id":381,"date":"2021-07-26T00:00:00","date_gmt":"2021-07-26T00:00:00","guid":{"rendered":"https:\/\/tac.debuzzify.com\/?p=381"},"modified":"2023-06-27T05:31:24","modified_gmt":"2023-06-27T05:31:24","slug":"how-to-detect-memory-leakage-in-your-python-application","status":"publish","type":"post","link":"https:\/\/www.the-analytics.club\/how-to-detect-memory-leakage-in-your-python-application\/","title":{"rendered":"How to Detect Memory Leakage in Your Python Application"},"content":{"rendered":"\n
Standard Python libraries that could tell the memory usage and execution time of every line<\/i><\/b><\/p>\n\n\n\n
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It\u2019s interesting to see how we improved measuring algorithm performance in Python. About a decade ago, when I started coding in Python, I stored time into variables at different points in my code. It is the ugliest way, for sure, but at that time, I thought I was smart.<\/p>\n\n\n\n
A couple of years later, when I learned to use decorators in Python, I created a function to do the same. I thought I got smarter.<\/p>\n\n\n\n
But the Python ecosystem has grown huge in the last decade. Its applications spread beyond data science and web app development. Along with this evolution, we improved the ways to do performance audits in Python.<\/p>\n\n\n\n
The need for a more accurate measure of resource usage is high in the era of cloud computing. If you\u2019re using AWS, Azure, G-Cloud, or any other cloud infrastructure, often you\u2019ll have to pay for resource hours.<\/p>\n\n\n\n
Also, Python is the prevalent language for data-intensive applications such as machine learning and distributed computing. Thus, understanding profiling and performance auditing is essential for every Python programmer.<\/p>\n\n\n\n
Before moving on, let\u2019s also discuss the old-school methods I\u2019ve been using for years.<\/p>\n\n\n\n
Grab your aromatic coffee <\/a>(or tea<\/a>) and get ready…!<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n This method was my approach when I first started programming. I store the time values before and after the execution of a function. The difference is how long the process ran.<\/p>\n\n\n\n The below code snippet counts the number of prime numbers lesser than the input value. In the function at the beginning and at the end, I\u2019ve written codes to capture time and calculate duration. If I need to code another function that requires a performance audit, I\u2019ll have to do the same again.<\/p>\n\n\n\nThe old-school methods I\u2019ll never use again.<\/h2>\n\n\n\n