Are you tired of hearing about garbage collection (GC) but still don’t quite understand how it works?

To begin with: what is garbage collection? It’s basically a way for your computer to automatically clean up memory that you no longer need.In Python, there are two main types of GC implementations the one used by CPython (the default implementation) and the one used by PyPy (an alternative interpreter).

CPython uses what’s called a reference counting system for garbage collection. This means that every time you create an object in Python, it gets assigned a “reference count” which keeps track of how many other objects are pointing to it. When this reference count goes down to zero (meaning no one is using the object anymore), CPython will automatically delete it from memory.

On the other hand, PyPy uses what’s called a garbage collector that works in two phases: mark and sweep. In the first phase, the GC marks all of the objects that are still being used (i.e., have references pointing to them). Then, in the second phase, it sweeps through memory and deletes any unmarked objects.

So which one is better? Well, both have their pros and cons. CPython’s reference counting system can be faster for small programs because it doesn’t require a separate garbage collector thread to run. However, as your program gets larger and more complex, the reference count system can become less efficient due to fragmentation (when memory is broken up into smaller chunks that are harder to manage).

PyPy’s mark-and-sweep GC, on the other hand, can be slower for small programs because it requires a separate thread to run. However, as your program gets larger and more complex, PyPy’s GC becomes more efficient due to its ability to handle fragmentation better than CPython’s reference counting system.

In terms of memory usage, both implementations have their own trade-offs. CPython can use less memory because it doesn’t need a separate garbage collector thread (which requires additional memory). However, PyPy’s GC can be more efficient in managing fragmentation and reducing overall memory usage over time.

So which one should you choose? Well, that depends on your specific needs! If you have a small program with simple memory requirements, CPython might be the better choice due to its faster reference counting system. However, if you’re working on a large or complex project, PyPy’s GC can provide more efficient memory management over time.