Скачать или смотреть Python Memory leak debugging

Memory leaks are a common issue in software development, and Python is no exception. A memory leak occurs when a program fails to release memory it no longer needs, causing the application to consume more and more memory over time. In this tutorial, we will explore how to identify and debug memory leaks in Python, with code examples using common tools.
Before we dive into debugging memory leaks, make sure you have the following prerequisites:
The first step in debugging memory leaks is to profile your code to identify where memory is being allocated and not released. Python provides several tools for profiling, including cProfile and memory_profiler. For this tutorial, we'll use memory_profiler.
You can install memory_profiler using pip:
Let's assume you have a Python script named leaky_app.py. To profile your code, add the following lines to the script:
Run your script with the mprof command:
This will generate a mprofile_*.dat file that contains memory usage information. Run your script and perform actions that you suspect might be causing memory leaks.
Now that you've profiled your code, it's time to analyze the memory profile data. Run the following command to see the memory usage over time:
This will generate a plot that shows memory usage over time, making it easier to identify memory leaks.
Look for patterns in the memory usage plot. Identify areas in your code where memory usage steadily increases over time. This is a strong indicator of a memory leak.
Once you've identified the source of the memory leak, you can use specialized tools to investigate further. Some useful tools include:
objgraph is a Python library that helps visualize object references. You can install it using pip:
Use objgraph to find objects that are not being garbage collected: