Python Memory Management
In Python, when a program exits or a script finishes running, the operating system automatically reclaims the memory used by the program. However, it’s important to note that not all memory may be immediately released or freed. This behavior is not specific to Python but rather a characteristic of how modern operating systems manage memory.
The reason for this is related to performance optimization. Releasing memory back to the operating system involves additional overhead, such as updating memory allocation tables, bookkeeping, and potential disk I/O. To avoid this overhead and improve efficiency, the operating system often takes a different approach.
Instead of returning each individual memory allocation to the operating system, Python (and other programming languages) use a memory manager that operates at a higher level. This memory manager, provided by the operating system, allocates chunks of memory to the Python process. Python, in turn, manages this memory internally using its own memory management system.
When the Python interpreter exits, it releases the memory it has allocated to the operating system, but it may not necessarily release every individual memory allocation. Some memory allocations may still be held by the memory manager for future use. The operating system treats the released memory as available for other processes, but it doesn’t necessarily guarantee that the memory is entirely freed.
It’s also worth noting that some memory leaks or mismanagement within a Python program can cause memory to be held onto even after the program has exited. This situation occurs when objects or data structures are not properly deallocated or when there are circular references that prevent garbage collection. In such cases, the memory may not be released until the operating system reclaims it.
To ensure proper memory management in your Python programs, it’s good practice to explicitly release any resources you allocate, close open files, and properly handle object cleanup. Python provides mechanisms like the del statement and the __del__ method (although it’s not recommended to rely on __del__ for memory management) to help with this task. Additionally, using tools like garbage collectors or profiling tools can help identify and resolve memory leaks.
Objects referenced from the global namespaces of Python modules are not always deallocated when Python exits. This may happen if there are circular references. There are also certain bits of memory that are allocated by the C library that are impossible to free (e.g. a tool like Purify will complain about these). Python is, however, aggressive about cleaning up memory on exit and does try to destroy every single object.
If you want to force Python to delete certain things on deallocation, you can use the atexit module to register one or more exit functions to handle those deletions. Read about atexit module.