The third (generation 2) generation is final, and the object stays there if it has been reached. When the generational garbage collection runs and does not remove the object, it gets promoted to the next generation. When a new object is created, it gets assigned to the first generation (generation 0, because we count from 0). Object generationsĮvery object in Python belongs to one of the three generations of objects.
Such a garbage collection depends on the concept called object generations and configurable thresholds. As you see, the process of checking whether an object is reachable is not as straightforward as counting references, so it is not as fast as deallocating memory when reference count drops to zero.īecause of that, Python does not run the generational garbage collector every time a reference is removed. The garbage collector can safely remove objects which are not reachable from the application code. If a reachable object has a reference to another object, the second object also becomes reachable.
If there is a variable pointing at the object, it is reachable. To avoid problems with cyclic references, the generational garbage collector checks whether the object is reachable from the application code. Generational garbage collector Memory deallocation Parsing machine learning logs with Ahana, a managed Presto service, and Cube, a headless BI solutionĬheck out my article published on the v blog! Because of that generational garbage collector was added to Python. In such situations, the objects the reference counting will never deallocate the memory used by those objects even if no variables are pointing to any of them. Object B references object C, and object C references object A again. A cyclic reference happens when an object references itself, or a few objects reference each other in such a way that object A references the object B. The reference counting implementation is fast and easy, but it is too simple to deal with cyclic references. Of course, when the value gets removed, the variables it referenced will get their reference counts decreased, and as a consequence, more memory may be deallocated. When the counter reaches zero, the garbage collector deallocates the memory occupied by the value, because the value is no longer used. Similarly, when you remove the value from a collection, the program exits a function (so it cannot use the parameter anymore), you set the value to None, override it with a different value or remove the variable (using the “del” statement), the counter gets decreased. The counter is used to count references to the value.Įvery time you assign the value to another variable or a property of an object, pass it as a parameter or add the value to a collection, the counter gets increased. When it happens, the garbage collector not only gives you the memory you need to store the value, but it also creates a counter. Reference countingĮvery time you create a new value (whatever it is, a number, string, object, etc.) Python needs to allocate memory for that new value. The memory deallocation mechanism relies on two implementations: reference counting and generational garbage collection. A garbage collector is a module responsible for automated allocation and deallocation of memory.
Python free memory code#
In this article, I am going to show you how memory management works in Python, and how it affects your code running in Jupyter Notebook.įirst, I have to describe the garbage collection mechanism.
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