Memory Handling and Garbage Collection in Python
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The document discusses memory handling and garbage collection in Python. It describes the three categories of memory for data storage: static, run-time stack, and heap. Problems can occur with heap storage in the form of memory leaks and dangling pointers. Garbage collection aims to solve these issues by identifying inaccessible blocks of storage and returning them to the free list. The three major approaches to automated garbage collection are reference counting, mark-sweep, and copy collection.
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Memory Handling and Garbage Collection in Python
- 1. Memory Handling & Garbage Collection in Python Prepared by: Ms. Pooja Mehta ITSNS Branch, GTU-CDAC-BISAG ME Program, Gandhinagar 1
- 2. Memory Management 3 March 2016By: Pooja Mehta 2 The process of binding values to memory locations Whether done automatically (as in Python), or partially by the programmer (as in C/C++), dynamic memory management is an important part of programming language design.
- 3. Three Categories of Memory (for Data Storage) 3 March 2016By: Pooja Mehta 3 Static: storage requirements are known prior to run time; lifetime is the entire program execution Run-time stack: memory associated with active functions Structured as stack frames Heap: dynamically allocated storage; the least organized and most dynamic storage area
- 4. Cont.. 3 March 2016By: Pooja Mehta 4
- 5. Cont.. 3 March 2016By: Pooja Mehta 5
- 6. Memory Leaks and Garbage Collection 3 March 2016By: Pooja Mehta 6 Three types of storage Static Stack Heap Problems with heap storage: Memory leaks (garbage): failure to free storage when pointers (references) are reassigned Dangling pointers: when storage is freed, but references to the storage still exist.
- 7. Garbage 3 March 2016By: Pooja Mehta 7 Any block of heap memory that cannot be accessed by the program; i.e., there is no stack pointer to the block; but which the runtime system thinks is in use. Garbage is created in several ways: A function ends without returning the space allocated to a local array or other dynamic variable. A node is deleted from a linked data structure, but isnt freed
- 8. Garbage Collection 3 March 2016By: Pooja Mehta 8 All inaccessible blocks of storage are identified and returned to the free list. The heap may also be compacted at this time: allocated space is compressed into one end of the heap, leaving all free space in a large block at the other end.
- 9. Implementing Automated Garbage Collection 3 March 2016By: Pooja Mehta 9 If programmers were perfect, garbage collection wouldnt be needed. There are three major approaches to automating the process: Reference counting Mark-sweep Copy collection
- 10. Reference Counting 10 Prior to Python version 2.0, the Python interpreter only used reference counting for memory management. Reference counting works by counting the number of times an object is referenced by other objects in the system. When references to an object are removed, the reference count for an object is decremented. When the reference count becomes zero the object is de allocated. 3 March 2016By: Pooja Mehta
- 11. Mark and Sweep 3 March 2016By: Pooja Mehta 11 Reference counting tries to find unreachable objects by finding objects with no incoming references. Imprecise because we forget which references those are.
- 12. Cont.. 3 March 2016By: Pooja Mehta 12 The root set is the set of memory locations in the program that are known to be reachable. Any objects reachable from the root set are reachable. Any objects not reachable from the root set are not reachable.
- 13. Cont.. 3 March 2016By: Pooja Mehta 13 Marking phase: Find reachable objects. Add the root set to a worklist. While the worklist isn't empty: Remove an object from the worklist. If it is not marked, mark it and add to the worklist all objects reachable from that object. Sweeping phase: Reclaim free memory. For each allocated object: If that object isn't marked, reclaim its memory. If the object is marked, unmark it (so on the next mark-and sweep iteration we have to mark it again).
- 14. Copy Collection 3 March 2016By: Pooja Mehta 14 Divide heap into two semispaces. Allocate from one space (fromspace) till full. Copy live data into other space (tospace). Switch roles of the spaces. Requires fixing pointers to moved data (forwarding). Eliminates fragmentation. DFS improves locality, while BFS does not require any extra storage.
- 15. References 15 https://docs.python.org/2/c-api/memory.html http://foobarnbaz.com/2012/07/08/understanding- python-variables/ https://www.digi.com/wiki/developer/index.php/Pyt hon_Garbage_Collection 3 March 2016By: Pooja Mehta
- 16. 16 Thank you...!
Editor's Notes
- #11: The user does not have to preallocate or deallocate memory by hand as one has to when using dynamic memory allocation in languages such as C or C++.
- #12: The easiest way to create a reference cycle is to create an object which refers to itself as in the example below: def make_cycle(): l = [ ] l.append(l) make_cycle() Reference counting is extremely efficient but it does have some caveats. One such caveat is that it cannot handle reference cycles. A reference cycle is when there is no way to reach an object but its reference count is still greater than zero.
- #14: Problems Memory Fragmentation Work proportional to size of heap Potential lack of locality of reference for newly allocated objects (fragmentation) Solution Compaction Contiguous live objects, contiguous free space
- #15: Automatic Garbage Collection of Cycles Because reference cycles are take computational work to discover, garbage collection must be a scheduled activity. Python schedules garbage collection based upon a threshold of object allocations and object deallocations. When the number of allocations minus the number of deallocations are greater than the threshold number, the garbage collector is run. One can inspect the threshold for new objects (objects in Python known as generation 0 objects) by loading the gc module and asking for garbage collection thresholds. Because make_cycle() creates an object l which refers to itself, the object l will not automatically be freed when the function returns. This will cause the memory that l is using to be held onto until the Python garbage collector is invoked