How do I find what is using memory in a Python process in a production system

in a production system, it's crucial to monitor and manage memory usage to ensure your python applications run efficiently and don't run into memory-related issues. this tutorial will guide you through the process of finding what is using memory in a python process using various tools and techniques.
we will cover the following topics:
python's psutil module provides an easy way to retrieve information about system utilization, including memory usage of a process. to use it, you'll need to install the psutil package if you haven't already:
now, let's create a python script to monitor memory usage:
this script retrieves the memory information for the current python process and prints the resident set size (rss) in kilobytes. you can schedule this script to run periodically to monitor memory usage over time.
python's tracemalloc module allows you to trace memory allocations in your code, which can be helpful in identifying memory-intensive areas. to use it, you don't need to install any additional packages; it's available in the python standard library.
here's a simple example:
in this example, we use tracemalloc to trace memory allocations within the some_memory_intensive_function. it then prints the top 10 lines of code responsible for memory usage. you can integrate this into your code to identify memory hotspots.
the ps and top commands are powerful tools for monitoring system resources, including memory usage. you can use them to inspect python processes running in production.
to find python processes and their memory usage:
this command lists all python processes, and you can identify their memory usage in the rss column. rss stands for resident set size, which indicates the amount of physical memory used by the process.
the memory_profiler package is a third-party tool for profiling memory usage in python programs. install it using pip:
here's how to use it: