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:
Python3
import tracemalloc tracemalloc.start() # Allocate some memory a = [ 1 ] * ( 10 * * 6 ) snapshot = tracemalloc.take_snapshot() top_stats = snapshot.statistics( 'lineno' ) for stat in top_stats[: 10 ]: print (stat) |
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.
Python3
import objgraph # Allocate some memory a = [ 1 ] * ( 10 * * 6 ) b = [ 2 ] * ( 10 * * 6 ) objgraph.show_most_common_types() |
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.
Python3
from memory_profiler import profile @profile def my_function(): a = [ 1 ] * ( 10 * * 6 ) b = [ 2 ] * ( 10 * * 6 ) del a del b my_function() |
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
Diagnosing and Fixing Memory Leaks in Python
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.