Why Doesn\'t Python Release All Memory After Garbage Collection?

Memory Management in Python: Understanding Memory Usage and Release

When working with Python, understanding memory usage is crucial. Consider the following example, where creating a list of 10 million elements initially increases memory usage to 80.9mb. After deleting the list, memory usage only decreases to 30.4mb, leaving 26mb unreleased.

Questions and Answers:

1. Why does Python not release all the memory after garbage collection?

Python optimizes memory usage by anticipating future memory needs. It holds onto some released memory in case additional memory is required in the near future.

2. What determines the specific amount of memory released (50.5mb in this case)?

The amount of memory released is not documented and is subject to internal implementation details of the Python memory manager.

3. Can we force Python to release all used memory?

No, there is no direct way to force Python to release all memory. However, a workaround is to create a child process to perform memory-intensive tasks. When the child process terminates, the allocated memory is automatically released. This can be implemented using libraries such as concurrent.futures or multiprocessing.

Additional Considerations:

• Creating child processes for frequent memory-intensive tasks can incur some overhead, including process startup time and potential data transfer costs.
• Sending large amounts of data between processes requires pickleability or other data transfer methods.

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