Check A Quick Guide On Memory Leak In Python

Check A Quick Guide On Memory Leak In Python

Check A Quick Guide On Memory Leak In Python

Many people might have experienced the situation when the program took longer than required. Or maybe the program even consumed more space than it assumed to take. A situation like this in programming can be a memory leak. Though it’s not necessarily a memory leak, this is one of the top problems that developers experience. Therefore, we decided to make a separate article on this. To demonstrate a memory leak in Python, we will be using the Python programming language today. Therefore, keep reading this article to get the best knowledge regarding memory leak in Python. So let’s start our article.

Memory Leak In Python

Let’s start by knowing the definition of “memory leak in python”.

A memory leak occurs when software or an application holds a computer’s primary memory for a long period of time. It happens when a local memory application does not return. Or when discharge allocated memory space after execution. It causes the system to become slowed or unresponsive.

A space leak is another name for a memory leak.

A memory leak is seen as a flaw or problem in the application or software that contains it. The application/program may maintain the application in memory to conduct actions. Or stay frozen in an unrecoverable state, which may be intentional or unintentional memory leaking. Additionally, the local software may source/leak new memory space without freeing previously utilized space. This results in memory resource exhaustion and a slow or frozen system.

The garbage collector continuously eliminates unneeded memorization.

However, in practical terms, it is not as simple as it appears. Garbage collectors may fail to check for unreferenced objects. This results in a memory leak in Python. Python scripts eventually run out of memory as a result of memory leaks. Finding memory leaks in Python and then fixing them becomes difficult.

Therefore, a memory leak in Python happens when useless data accumulates and the programmer forgets to erase it. To identify memory leaks in Python, we must execute a memory profiling procedure. Here, we can measure the amount of memory consumed by each portion of the code.

Memory leak in Python are caused by the following factors

  • To keep all the massive things that haven’t been released yet: Whenever the domain controller is unable to replicate for a while, the objects arise.

Following that, the domain controller reconnects to the replication topology. When you delete an object from the active directory service while the domain controller is down, the item remains as an object on the domain controller. Memory leaks are caused by persistent things that eat space.

  • Memory leaks can also cause by reference cycles in the code: A reference contains the address and class information for the objects it refers to. Assigning references doesn’t produce different duplicate objects. However, memory leaks occur when an object is no longer in use and can’t be garbage collected. This is because it references elsewhere in the program.
  • Memory leaks can also cause by underlying libraries: Python makes use of a number of libraries for data modeling, visualization, and processing. Python libraries really connect to memory leaks, despite the fact that they make Python data processing considerably easier.

There’s no need to concern about a memory leak in Python when you’re a Python coder. Python automatically alerts the garbage collector to remove any unreferenced data-related trash from memory.

Although memory leaks can occasionally resolve automatically by the garbage collector. But this is not always the case. That is why you’ll need to use various techniques to resolve any issues related to a memory leak.

Methods to eliminate memory leak in Python.


The new Trace malloc built-in module is Python’s best feature. Because it seems to be the best solution for the problem of memory leaks in Python. It will help you connect an object to the location where it initially assigned.

It includes a stack trace that may use to determine which use of a common function is eating memory in the program. You may use Trace malloc to keep track of an object’s memory usage. In the end, you’ll be able to figure out what causes memory leak in Python. Therefore, if you know which items are causing memory leaks, you may either fix them or delete them.

It will effectively minimize the program’s memory footprint. That’s why Tracemalloc is recognised as Python’s most powerful memory tracker approach for preventing memory leaks.

The Python TRACEMALLOC environment variable should be set to 1 or the -X tracemalloc command-line option. And it should start the module as soon as possible to trace most memory blocks allocated by Python. To begin tracking Python memory allocations, invoke the tracemalloc.start() method at runtime.

Memory leaks can be fixed using debugging methods.

You’ll have to use the garbage collector included module to debug memory usage in Python. This will provide you with a list of things that the garbage collectors are aware of.

Debugging allows you to view where the majority of the Python storage memory is using. After that, you may filter things depending on their use.

If you come across items that aren’t in use but referenced, you can delete them to minimize memory leaks.

Let’s wrap it up!

In today’s article, we learned about a memory leak in the Python programming language. We told you about it in detail. After that, we saw three situations where the possibility of having a memory leak was highest.

Also, we saw two ways in which you can easily resolve the problem related to a memory leak in the Python programming language. Now we hope you have understood the concept related to memory leakage. But if you want to ask something related to memory leaks or programming, then please contact us. We always welcome any email or message requesting help or suggestions.

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