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| | Garbage Collection in Python. (14th Apr 25 at 6:58am UTC) | | In Python, garbage collection is automatically reclaiming memory that is no longer needed. Python employs both reference counting and cyclic garbage collection to reclaim memory. Let us explore each of them in detail:
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1. Reference Counting
Python maintains the number of references to an object. Every object stored in memory also has a corresponding reference count, which is increased when a reference is made to the object and decreased when a reference is undone.
If an object's reference count reaches zero, then no references are remaining to the object, and its memory can be released.
2. Cyclic Garbage Collection
Although reference counting is helpful, it is not without its limitations. It cannot cope with circular references — where two or more objects point to one another, such that their reference counts never go down to zero. To overcome this, Python implements cyclic garbage collection.
Cyclic garbage collection is implemented by identifying and garbage collecting objects which are part of reference cycles (where objects point to one another in a loop).
The gc module in Python enables you to communicate with the garbage collector.
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3. The gc Module
Python has a gc module that allows interaction with the garbage collection process. Some of the significant functions in the gc module are:
gc.collect(): Trigger a garbage collection cycle.
gc.get_count(): Return the current counts of collections for each generation.
gc.get_stats(): Return detailed statistics of the garbage collector.
gc.set_debug(): Set debugging flags to regulate the verbosity of garbage collection.
4. Generational Garbage Collection
Python's garbage collector adopts a generational model to enhance performance. Objects are separated into generations (young, middle-aged, and old), and garbage collection is performed in phases depending on the age of the object.
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Python Garbage Collection | |
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