Causes of Memory Leaks in Python | How to Avoid Them? (2023)

Memory leaks in Python is a critical issue. Even when writing codes for any programming language, memory is one of the most important parts. A programmer learns to interact with the memory at the lowest level. It helps in maintaining the efficient working of a program. Programmers often have to deal with problems like memory leaks. Memory leaks are terrible as they block resources of memory and degrade the performance of the applications. Programs usually run out of memory because of the unused references that were not deleted. This issue happens when the garbage collector cannot clean and remove the unreferenced data from a program.

Python language is prone to such memory leaks. Therefore, it becomes a challenge for the programmers and developers to identify and resolve the errors. Keep reading this article if you want to know more about the causes and solutions of memory leaks.

Table of Contents

Memory Leaks in Python

What is a memory leak?

When a programmer creates a memory in a heap and forgets to delete it, it leads to a memory leak. It is a resource leak. Due to this, the program’s available memory gets exhausted and leads to the destruction of the program. It fills up the storage of the application unnecessarily and makes the working of the program slow. Memory leaks are also outcomes of program bugs.

Memory leaks can be detected in many ways. Some applications provide memory leak detection. They help in the detection of the bug preventing the application from crashing. There are programming tools that provide memory allocation and garbage collection.

Memory leaks in python programming

Like any other language, Python also has a memory. When a programmer fails to delete unused objects in the memory, the memory gets filled up. These objects leak into the used memory and cannot be removed. Underlying libraries or C extensions, lingering large objects which are not released, and reference cycles within the code can cause memory leaks. Thus we can say that memory leaks are caused when objects no longer in use are still maintained.

Memory management is an application in Python which reads and writes data. It is a tool that helps in resolving the issue of memory leaks. It uses reference counting in the default implementation of Python and CPython. Its main objective is to ensure the memory’s efficiency by checking that as soon as all the references to an object are expired, the referenced object is released too.

Causes of Memory Leaks in Python

CPython is a built-in detector in Python that ensures that the self-referencing objects and unused data are finally garbage collected.

In principle, this means that there is no need for the programmers to take care of the program’s allocation and deallocation of the memory. The CPython runtime will automatically take care of it by informing the garbage collector to remove the memory’s unused data.

However, this does not happen. Eventually, programs do run out of memory because of the held references. Sporadically, the garbage collector fails to monitor unreferenced objects. It is how memory leaks occur in Python. Slowly, the program fills up with memory leaks and runs out of memory. Detecting where the programs are leaking or using memory becomes a big challenge for programmers.

In short, memory leaks occur when unused objects get heaped up, and the programmer forgets to remove them. To detect and fix these problems, the programmers need to perform some Memory Profiling. It is the process through which the memory used by each piece of code is measured.

Memory profiling is not as intricate as it may sound. Basic memory profiling is extremely easy. The steps to quickly profiling, analyzing, and fixing the python code to detect the memory leaks in the code parts are given below.

How to Find Memory Leaks in Python


Primarily we can debug the memory usage by the GC built-in module. The GC built-in module provides a list of all the objects presently known by the garbage collector. Even though this is a blunt tool, it quickly gives an idea of where the program’s memory is being used. Then the objects can be filtered according to their use. It helps us identify the unused objects that are referenced and hence can be deleted by the programmer. Thus, preventing memory leaks in Python.

The debugger will provide information about how many objects were created during the execution process. The only problem with GC is that it does not provide any information about how the objects are being allocated. The more important thing here is to detect the code responsible for this error rather than several objects created. So, finally, this would be of no use in identifying the code causing memory leaks.

Ways to Fix Memory Leaks in Python

Memory profiling with tracemalloc

Tracemallocis a new built-in module introduced by Python 3.4. It is considered the most suitable for solving memory leaks and is extremely useful. It provides very detailed and block-level traces of the memory allocation. Apart from that it gives the connection of an object directly with the place where it was first allocated. It proves to be very useful. It is because it makes it easier for us to identify the code, which is the cause of memory consumption.

Tracemalloc gives the subsequent information:

  1. Traceback to where memory was allocated, including the file and line number
  2. Statistics for the overall behavior of the program.
  3. Calculation of the differences between 2 snapshots

The first step is to trace the memory usage of the entire program. It helps in identifying the objects that are using the most memory. Tracemalloc also provides an insight into the parts of the program which need attention.

It then takes a snapshot of the memory currently allocated in the Python heap usingtracemalloc.take_snapshot(). It saves information about the allocation memory, the source of the memory allocation, and their sizes. Tracemalloc helps in identifying which lines of code are allocated with which block of memory. We can calculate the statistics on memory use, differentiate the snapshots, and save them for analysis later.

This technique does not give adequate knowledge about which block of memory is used and where it is used.

We can find out the net memory usage between two snapshots while comparing them. The output does not show the memory created and deleted between the snapshots. The memory allocations visible in the differences between the snapshots are taken simultaneously and contribute to the entire memory used. They are not just a temporary allocation at the time of execution.

For reference cycles, only the uncollected cycles are shown in the output instead of the collected cycles. The objects deallocated by the garbage collector in the time taken by the snapshot are called freed memory. Hence, noise in the output can be reduced by forcing garbage collection before taking the snapshots.

While looking for a memory leak, we need to understand how the program’s memory usage is modified over time. To see the amount of memory allocated in each iteration, we can instrument the component’s main loop. When slowly small allocations start adding up, memory leaks become more apparent.

After this, only the snapshots having memory allocation related to the requests package can be kept. Finally, memory allocations can be tracked down by determining the uses of requests which are leaking memory. The full traceback helps in tracing backward from a memory allocation to its source. After identifying the errors in the code, the memory leaks can be fixed quickly.


Python is one of the most efficient and best programming languages in the world. It is an easy language designed for the readability of the mind. Many big projects like Google and Youtube have their code written in this language. Like other languages, memory leaks often occur in Python. Its built-in detector, CPython, helps in memory management. However, memory leaks still occur sometimes due to some unresolved issues.

It is a challenge for us programmers to solve this issue. By keeping track of the object’s memory usage and using the memory management model, the programs’ memory footprint can be reduced significantly. Python’s efficient memory tracking tool, tracemalloc, can quickly detect and fix memory leaks. Hence, being aware of the methods mentioned above can help us, programmers, resolve memory leaks quickly.


Causes of Memory Leaks in Python | How to Avoid Them? ›

One of the most common causes of memory leaks in Python is the retention of objects that are no longer being used. This can occur when an object is referenced by another object, but the reference is never removed. As a result, the garbage collector is unable to deallocate the unused object, leading to a memory leak.

What is the main cause of memory leaks? ›

DEFINITION A memory leak is the gradual deterioration of system performance that occurs over time as the result of the fragmentation of a computer's RAM due to poorly designed or programmed applications that fail to free up memory segments when they are no longer needed.

What is memory leak How can we avoid memory leak? ›

Memory leak in brief

More elaborately, it means losing reference to allocated memory that forbids further access and makes deallocation impossible. It typically happens when a computer program fails (or „forgets”) to delete certain, no longer needed, objects from the memory it had allocated there before.

How do you prevent memory errors in Python? ›

To avoid and fix memory errors, it's important to be aware of the memory usage of our programs and take necessary steps to manage our memory, such as using generators in place of lists, deleting unused objects, and using external storage. By being mindful of memory usage, we can write more efficient Python programs.

Are memory leaks common in Python? ›

Memory leaks are a common problem in software development, including Python. A memory leak occurs when a program fails to release memory that is no longer needed, resulting in the program consuming more and more memory over time, eventually leading to a crash or system failure.

What is memory leak 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.

Which of the following should be avoided to prevent memory leaks? ›

In Android development, using the activity-context to show a Toast is a common mistake that can easily lead to a leak. To avoid these leaks, bypass the application-context. The application-context is also used in the Toast documentation examples.

What makes Python memory safe? ›

A memory-safe language is a language where memory allocation and garbage collection are abstracted away from the developer and handled by the programming language itself. These languages include Python, Java, and Go, to name a few.

How we can manage memory in Python? ›

We use the Heap for implement dynamic memory management. We can use the memory throughout the program. As we know, everything in Python is an object means dynamic memory allocation inspires the Python memory management. Python memory manager automatically vanishes when the object is no longer in use.

How do you control memory in Python? ›

Memory allocation can be defined as allocating a block of space in the computer memory to a program. In Python memory allocation and deallocation method is automatic as the Python developers created a garbage collector for Python so that the user does not have to do manual garbage collection.

Does exception cause memory leak? ›

The pointer to the object goes out of scope when the exception leaves the function, and the memory occupied by the object will never be recovered as long as the program is running. This is a memory leak; it would be detected by using the memory diagnostics.

What are the common errors that are seen in memory leak? ›

Types of Errors

"probably lost": your program is leaking memory, you have not free()'d some allocated memory or you have changed the pointer to the object and hence, lost the original pointer. If you see this on OSX, it may be a false positive.

How do you troubleshoot memory problems in Python? ›

  1. Step 0 — What is memory, and what is a leak? ...
  2. Step 1: Establish that it is a memory problem. ...
  3. Step 2: Reproduce the memory problem locally with a minimal example. ...
  4. Step 3: Find the lines of code that are allocating the most memory. ...
  5. Step 4: Identify Leaking objects. ...
  6. Step 5 — Verify the fix works.

How do I find out where my memory leak is coming from? ›

The simplest way to detect a memory leak is also the way you're most likely to find one: running out of memory. That's also the worst way to discover a leak! Before you run out of memory and crash your application, you're likely to notice your system slowing down.

What is memory leakage in the brain? ›

Memories leak out from otherwise segre- gated systems and affect the processing of different types of memory (skills vs. facts). A leak can disrupt the processing of another different type of memory and so modify the fate of the memory in a binary way (impaired vs. retained).

Where are memory leaks found? ›

Where are memory leaks found? Explanation: Memory leaks happen when your code needs to consume memory in your application, which should be released after a given task is completed but isn't. Memory leaks occur when we are developing client-side reusable scripting objects.

Do memory leaks cause permanent damage? ›

All of this leads to unviable software and unplanned systemic misfunctions. Physical or permanent damage does not happen from memory leaks. Memory leaks are strictly a software issue, causing performance to slow down among applications within a given system.


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