Adventures in Machine Learning

Optimizing Memory Management in Python: A Guide to Garbage Collection

Introduction to Garbage Collection in Python

Garbage collection plays a crucial role in managing memory in programming languages like Python. It is responsible for freeing up memory that is no longer required by the program, preventing memory leaks and improving performance.

In this article, we will explore the basics of garbage collection in Python, including reference counting and how it works, reference cycles, and when garbage collection occurs.

Reference Counter in Python

Python uses a reference counter to manage memory, which counts the number of references to an object. An object will remain in memory for as long as there is a reference to it.

Once the reference count falls to zero, Python frees up space used by the object immediately. This means that as a programmer, you don’t need to manually manage memory in Python like you would with other programming languages like C++.

Python automatically increases the reference count of an object when a new reference is created and decreases it when a reference is deleted. Reference counting in Python works well for many situations, especially for short-lived objects.

For longer-lived objects and complex structures, reference counting is not enough. This is where garbage collection comes in.

How Garbage Collection Works in Python

Garbage collection in Python works by periodically identifying and freeing up objects that are no longer in use. Python provides a module called `gc` that allows you to control and customize garbage collection.

Python garbage collection uses the reference count of objects as the primary basis for object destruction. When the reference count of an object reaches zero, it is immediately scheduled for deallocation.

However, reference cycles or circular references make this method inadequate and can result in memory leaks.

Reference Cycles

A reference cycle happens when two or more objects reference each other. For example, consider two objects A and B, where A points to B and B points back to A.

Since both objects have a reference count greater than zero, they will never be deallocated by Python’s garbage collection system. In such cases, Python’s garbage collector periodically traverses all objects in memory and checks for reference cycles.

If the garbage collector finds a reference cycle, it marks the objects involved in the reference cycle as “uncollectible” and does not deallocate them.

When Garbage Collection Occurs

Garbage collection in Python happens in the background without any direct involvement from the programmer. Python starts by allocating new memory whenever the program creates a new object.

When Python runs out of memory, the garbage collector kicks into action and begins to free up space. The garbage collector monitors the heap space and triggers automatically when a threshold is reached.

The threshold is a certain amount of memory consumption that indicates that it is time to perform a garbage collection cycle. The garbage collector can also be manually invoked at any time using the `gc.collect()` method.


In summary, garbage collection is an essential feature for managing memory in Python. To understand it, you need to know about reference counting and reference cycles, how Python uses reference counting for garbage collection, and how the Python garbage collector works.

As a programmer, you don’t have to worry about memory management in Python, but it’s essential to understand how Python garbage collection works to optimize your code’s performance and avoid memory leaks.

3) Manually Working with Garbage Collection

Python has a well-designed garbage collection mechanism that automatically manages memory for the programmer, but sometimes it becomes necessary to manually work with the system to optimize performance. The `gc` module provides a way for the programmer to manage garbage collection.

In this section, we will explore how to disable and enable garbage collection, create a class to test garbage collection, delete objects, and trigger garbage collection manually.

Disabling and Enabling Garbage Collection

There may be a circumstance where you do not want Python to perform garbage collection. Python’s garbage collection system can be disabled using the `gc.disable()` function.

This function can be used to disable garbage collection at runtime, and garbage collection can be enabled again using the `gc.enable()` function. It is essential to be aware that disabling garbage collection can lead to a memory leak if you do not manage it well manually.

In most cases, it is recommended to let garbage collection run naturally.

Creating a Class to Test Garbage Collection

The `gc` module provides methods that allow you to track the lifecycle of objects in memory and monitor the garbage collection process. One such method is `gc.get_objects()`, which returns a list of all objects in memory at the point it is called.

We can create a simple class to demonstrate how Python’s garbage collection works. The `Track` class below is based on Python’s `built-in` `list`, which is a good example here since it defines a simple class and allocates memory for objects.


import gc

class Track(list):

def __del__(self):

print(f”Removing {self} at {hex(id(self))}”)


t1 = Track()

t2 = Track()




# Removing object t2

del t1




The above code creates an instance of the `Track` class and appends it to another instance of the same class. This creates a reference cycle, which, without special handling, would prevent the garbage collector from marking the objects as uncollectible.

`gc.get_objects()` prints all objects known to Python and shows that t1 and t2 are still in memory. `Removing object t2` is printed when we delete `t1`, as this is where the reference cycle initiated.

After that, the final call to `gc.get_objects()` shows that neither `t1` nor `t2` exists anymore, so the objects have been successfully removed from memory.

Deleting Objects

In Python, objects can be deleted using the `del` statement. Removing an object from memory manually can be useful when you know an object is no longer required, but you do not want to wait for garbage collection to free up the memory.

Additionally, it is often best to rely on Python’s garbage collector to manage memory rather than opting to delete objects manually. If you delete an object incorrectly or prematurely, it can lead to undefined behavior and crashes.

Triggering Garbage Collection Manually

Python’s garbage collection can be invoked manually using the `gc.collect()` function. Calling this function will start a garbage collection cycle that frees up memory as needed.

However, it is not necessary to invoke the `gc.collect()` function manually in normal circumstances. Triggering garbage collection manually can be useful in situations where you want to run a memory profiler, analyze the contents of your program’s heap space, or troubleshoot any issues associated with memory management.

4) Reasons for Garbage Collection Not Taking Place

Garbage collection is a critical part of memory management in Python, but sometimes it may not run correctly. Several reasons may cause garbage collection not to take place as expected.

Memory Limitations and Garbage Collection Issues

Python’s garbage collection mechanism works by monitoring the heap space of the process where objects are allocated. If the heap space is fully consumed, this can cause the Python interpreter to crash, resulting in exceptions like `MemoryError`.

When this happens, garbage collection may not be triggered correctly, leading to memory issues in the program. Other issues that can cause garbage collection problems include mismanagement of circular references, incorrect use of weak references, or the excessive use of large or persistent objects that cannot fit in the available memory.


In conclusion, manual management of Python’s garbage collection mechanism is essential for optimizing memory usage, profiling memory usage, and troubleshooting memory management issues. While Python’s garbage collection mechanism is designed to handle memory management automatically and efficiently, it may not always work as expected.

By disabling and enabling garbage collection, creating classes to test garbage collection, deleting objects, and triggering garbage collection manually, programmers can gain a deeper understanding of how Python’s garbage collection system works and take necessary steps to optimize memory management. In conclusion, Python’s garbage collection mechanism plays a vital role in managing memory and preventing memory leaks in programming.

The use of a reference counter, reference cycles, and a background garbage collector ensures that memory is managed efficiently without the need for manual intervention. However, in some cases, it may be necessary to work manually with garbage collection by disabling and enabling garbage collection, creating classes to test garbage collection, deleting objects, and triggering garbage collection manually to optimize memory usage, profile memory usage, and troubleshoot memory management issues.

It is essential to have a deeper understanding of how Python’s garbage collection system works and to take necessary steps to ensure optimized memory management for better program performance and scalability.

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