Python manages memory automatically through a combination of reference counting, garbage collection, and memory pools. Here’s a breakdown of how Python handles memory efficiently:
1. Reference Counting
Python uses reference counting to track the number of references to an object in memory.
- Each object has an associated reference count.
- When the count drops to zero, the memory is deallocated automatically.
2. Garbage Collection (GC)
Python has automatic garbage collection to clean up unused objects that are not referenced anymore, especially cyclic references.
How it Works:
- Python’s gc module tracks objects with cyclic references.
- Uses a generational garbage collector to optimize performance.
- Runs periodically in the background to free memory.
3. Memory Pools & Object Caching
Python optimizes memory usage by using memory pools to reduce frequent allocations and deallocations.
Key Components:
- Small Object Allocator (PyMalloc):
- Allocates small objects (≤512 bytes) efficiently using memory pools.
- Integer & String Caching:
- Small integers (from
-5 to 256) and short strings are cached to improve performance.
- Small integers (from
- Large Objects:
- Managed by the system’s memory allocator (via
mallocandfree).
- Managed by the system’s memory allocator (via