Memory management in Python involves a private heap containing all Python objects
and data structures. The management is handled by Python’s memory manager,
which ensures efficient allocation and deallocation of memory. Here are key
components and mechanisms involved in Python’s memory management:
Reference Counting:
Python uses reference counting to keep track of the number of references to each
object in memory. When an object’s reference count drops to zero, the memory
occupied by that object is deallocated.
Garbage Collection:
Besides reference counting, Python also has a cyclic garbage collector to detect and
clean up cyclic references (i.e., references that form a cycle and cannot be freed
solely by reference counting).
Memory Pools:
Python uses memory pools to manage small blocks of memory. Objects are allocated
and deallocated in pools to reduce fragmentation and improve performance.
Manual Memory Management:
While Python handles most memory management tasks automatically, developers
can influence memory management through techniques like using the ‘gc’ module to
manually trigger garbage collection.