How to Diagnose Memory Leaks in Python

There are several tools that can be used to diagnose memory leaks in Python. Here are a few options:

Tracemalloc module

The tracemalloc module is a built-in Python module that can be used to track the allocation of memory blocks in Python. It can be used to track the source code location where the memory was allocated, as well as the size of the allocated memory block.

To use tracemalloc, you will first need to enable it by calling tracemalloc.start(). Then, you can take a snapshot of the current memory allocation by calling tracemalloc.take_snapshot(). This will return a Snapshot object, which contains information about the memory blocks that are currently allocated.

You can then use the Snapshot.statistics() method to get a list of Statistic objects, which represent the memory blocks that are currently allocated, sorted by the size of the memory blocks. You can use this information to identify the source code locations where the largest memory blocks are being allocated, which may be indicative of a memory leak.

Here’s an example of how to use tracemalloc to take a snapshot and print the statistics:

Output:

6a0aa145-b72e-42a5-a8b2-b92294e6e4d9.py:6: size=7813 KiB, count=2, average=3907 KiB

Objgraph module

The objgraph module is a third-party Python module that can be used to visualize the relationships between objects in Python. It can be used to create a graph of the objects that are currently in memory, which can be helpful in identifying objects that are being retained unnecessarily, potentially leading to a memory leak.

To use objgraph, you will first need to install it using pip install objgraph. Then, you can use the objgraph.show_most_common_types() function to print a list of the most common object types that are currently in memory. You can also use the objgraph.show_backrefs() function to show the references to a specific object, which can be helpful in identifying objects that are being retained unnecessarily.

Output:

This will print a list of the most common object types, along with the number of instances of each type that are currently in memory.

function                   31059
dict                       17893
tuple                      13173
list                       9031
weakref                    6813
cell                       5321
builtin_function_or_method 4139
getset_descriptor          3808
type                       3598
method                     3467

Memory_profiler module

The memory_profiler module is a third-party Python module that can be used to measure the memory usage of Python code. It works by decorating functions or methods with the @profile decorator, which will cause the memory usage to be recorded at each point in the code where the decorated function is called.

To use memory_profiler, you will first need to install it using pip install memory_profiler. Then, you can decorate the functions or methods that you want to profile with the @profile decorator. When you run the code, the memory usage will be recorded at each point where the decorated function is called.

Output:

When you run this code, the memory usage will be recorded at each point where my_function is called. You can then use the recorded memory usage to identify any areas of the code where the memory usage is significantly increasing, which may be indicative of a memory leak.

Filename: c:\Users\siddhesh\demo.py

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
     3     21.9 MiB     21.9 MiB           1   @profile
     4                                         def my_function():
     5     29.6 MiB      7.6 MiB           1      a = [1] * (10 ** 6)
     6     37.2 MiB      7.6 MiB           1      b = [2] * (10 ** 6)
     7     29.6 MiB     -7.6 MiB           1      del a
     8     21.9 MiB     -7.6 MiB           1      del b

Memory leaks in Python can occur when objects that are no longer being used are not correctly deallocated by the garbage collector. This can result in the application using more and more memory over time, potentially leading to degraded performance and even crashing. In this article, we will explore how to diagnose and fix memory leaks in Python.