For those who are new to Python, you might be wondering what garbage collection even means and what its role is in programming. Put in simple terms, garbage collection is the process of automatically freeing up the memory used by objects that are no longer being referenced by your program. With Python being a dynamic typed language, garbage collection is crucial, since it helps optimize the memory usage of your program. Let's get started and explore this fascinating feature in more detail!

Table of Contents :

  • What is Garbage Collection
  • Reference Counts in Python
  • Garbage Collection in Python

What is Garbage Collection :

  • Garbage collection is an automatic memory management system that helps to automatically free up memory in a computer program.
  • Garbage collection algorithms automatically find and remove chunks of memory that are no longer in use.
  • The key advantage of garbage collection is that it prevents program crashes due to memory errors like segmentation faults.

Reference Counts in Python :

  • The Python language uses a system of reference counting to keep track of active memory usage.
  • The reference count system is implemented using a counter that is incremented when a new reference to a memory object is made and decremented when an object is no longer needed.
  • When the reference count reaches zero, the object is marked for garbage collection.
  • However, sometimes reference counts aren't perfect and can lead to hard-to-detect memory leaks.

Garbage Collection in Python :

  • Python's garbage collector is an automatic memory management system that automatically frees up memory that is no longer used.
  • Python's memory manager keeps track of object references and will destroy objects and reclaim used memory once an object's reference count reaches zero.
  • The garbage collector helps solve the issue of memory leaks that arise from circular references.
  • Python's  gc  module provides an interface for developers to interact with the garbage collector.
    • gc.disable() will stop the action of the garbage collector, 
    • gc.enable() will start garbage collector.
    • gc.collect() will force a garbage collection, but it may not free all the memory.
    • gc.get_count() returns the number of objects tracked by the garbage collector.
    • gc.set_threshold() sets the garbage collection thresholds, which specify the number of object allocations to allow before the garbage collector kicks in.
  • It's important to manage resources explicitly to free up memory when necessary, even with garbage collection. Implementing such best practices can help effectively leverage garbage collection and manage memory in Python applications.

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