Garbage Collection in Python

Python’s memory allocation and deallocation method is automatic. The user does not have to preallocate or deallocate memory similar to using dynamic memory allocation in languages such as C or C++. 
Python uses two strategies for memory allocation: 

1. Reference counting
2. Garbage collection

Reference counting

Python and various other programming languages employ reference counting, a memory management approach, to automatically manage memory by tracking how many times an object is referenced. A reference count, or the number of references that point to an object, is a property of each object in the Python language. When an object’s reference count reaches zero, it becomes un-referenceable and its memory can be freed up.

Examples :

Example 1: Simple Reference Counting

Example 2: Reference Counting with Cyclic Reference

Example 3: Using the sys.getrefcount() function

Output :

Reference count of x: 2

Garbage collection

Garbage collection is a memory management technique used in programming languages to automatically reclaim memory that is no longer accessible or in use by the application. It helps prevent memory leaks, optimize memory usage, and ensure efficient memory allocation for the program.

Generational Garbage Collection

When attempting to add an object to a reference counter, a cyclical reference or reference cycle is produced. Because the object’s reference counter could never reach 0 (due to cycle), a reference counter cannot destroy the object. Therefore, in situations like this, we employ the universal waste collector. It operates and releases the memory used. A Generational Garbage Collector can be found in the standard library’s gc module.

Automatic Garbage Collection of Cycles

Because reference cycles take computational work to discover, garbage collection must be a scheduled activity. Python schedules garbage collection based upon a threshold of object allocations and object deallocations. When the number of allocations minus the number of deallocations is greater than the threshold number, the garbage collector is run. One can inspect the threshold for new objects (objects in Python known as generation 0 objects) by importing the gc module and asking for garbage collection thresholds: 
 

Output: 

Garbage collection thresholds: (700, 10, 10) 

Here, the default threshold on the above system is 700. This means when the number of allocations vs. the number of deallocations is greater than 700 the automatic garbage collector will run. Thus any portion of your code which frees up large blocks of memory is a good candidate for running manual garbage collection. 

Manual Garbage Collection

Invoking the garbage collector manually during the execution of a program can be a good idea for how to handle memory being consumed by reference cycles. 
The garbage collection can be invoked manually in the following way: 

Output:

('Garbage collector: collected', '0 objects.')

If few cycles are created, then how manual collection works: 
Example: 

Output: 

Garbage collector: collected 0 objects.
Creating cycles...
{1: {...}}
{2: {...}}
{3: {...}}
{4: {...}}
{5: {...}}
{6: {...}}
{7: {...}}
{8: {...}}
{9: {...}}
{10: {...}}
Garbage collector: collected 10 objects.

There are two ways for performing manual garbage collection: time-based and event-based garbage collection. 

1. Time-based garbage collection is simple: the garbage collector is called after a fixed time interval. 
2. Event-based garbage collection calls the garbage collector on event occurrence. For example, when a user exits the application or when the application enters into an idle state. 

Forced Garbage Collection

In Python, the garbage collector runs automatically and periodically to clean up objects that are no longer referenced and thus are eligible for garbage collection. However, in some cases, you may want to force garbage collection to occur immediately. You can do this using the gc. collect() function provided by the gc module.

Example :

Disabling Garbage Collection

In Python, the garbage collector is enabled by default and automatically runs periodically to clean up objects that are no longer referenced and thus are eligible for garbage collection. However, in some cases, you may want to disable the garbage collector to prevent it from running. You can do this using the gc.disable() function provided by the gc module.

Interacting with Python Garbage Collector

A built-in mechanism called the Python garbage collector automatically eliminates objects that are no longer referenced in order to free up memory and stop memory leaks. The Python gc module offers a number of ways to interact with the garbage collector, which is often executed automatically.

1. Enabling and disabling the garbage collector: You can enable or disable the garbage collector using the gc. enable() and gc. disable() functions, respectively. Example:

2. Forcing garbage collection: You can manually trigger a garbage collection using the gc. collect() function. This can be useful in cases where you want to force immediate garbage collection instead of waiting for automatic garbage collection to occur. 

Example:

3. Inspecting garbage collector settings: You can inspect the current settings of the garbage collector using the gc.get_threshold() function, which returns a tuple representing the current thresholds for generations 0, 1, and 2. 

Example:

Output :

(700, 10, 10)

4. Setting garbage collector thresholds: You can set the thresholds for garbage collection using the gc.set_threshold() function. This allows you to manually adjust the thresholds for different generations, which can affect the frequency of garbage collection. 

Example:

Output :

Garbage collector thresholds set
Current thresholds: (500, 5, 5)

Advantages and Disadvantages 

Let’s explore some of the benefits and drawbacks of Python’s garbage collection.

Advantages :

1. Automated memory management: To avoid memory leaks and lower the chance of running out of memory, the Python garbage collector automatically removes objects that are no longer referenced.
2. Memory management made easier: The garbage collector frees developers from having to manually manage memory so they can concentrate on creating code, making Python a higher-level and more practical language for developers.
3. Efficient memory cleanup: The garbage collector is designed to minimise performance effects while swiftly identifying and collecting short-lived objects via generational garbage collection.
4. Customizable settings: The garbage collector provides options to customize its settings, such as adjusting the thresholds for different generations, allowing developers to fine-tune the garbage collection process based on their specific application requirements.

Disadvantages :

1. Impact on performance: Although the garbage collector is designed to efficiently clean up unused memory, there may still be some CPU consumption and execution time overhead, particularly when working with a large number of objects.
2. The difficulty of memory management: Although Python’s garbage collector makes managing memory easier, using it successfully may still necessitate knowledge of concepts like object lifetimes, object references, and garbage collection algorithms.
3. Limited control over memory management: The autonomous nature of the garbage collector leaves developers with little control over the precise timing and behaviour of memory cleanup, which may not be ideal for many application scenarios where fine-grained control over memory management is necessary.
4. Bug potential: Although the garbage collector is intended to be dependable and effective, it is not impervious to errors or atypical behaviour, which could lead to memory leaks or improper object cleanup.