When we create objects for classes, it requires memory and the attribute are stored in the form of a dictionary. In case if we need to allocate thousands of objects, it will take a lot of memory space.
slots provide a special mechanism to reduce the size of objects.It is a concept of memory optimisation on objects.
Example of python object without slots :
class GFG(object): Â Â Â Â Â Â def __init__(self, *args, **kwargs): Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â self.a = 1Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â self.b = 2Â Â if __name__ == "__main__": Â Â Â Â Â instance = GFG() Â Â Â Â Â print(instance.__dict__) |
Output :
{'a': 1, 'b': 2}
As every object in Python contains a dynamic dictionary that allows adding attributes. For every instance object, we will have an instance of a dictionary that consumes more space and wastes a lot of RAM. In Python, there is no default functionality to allocate a static amount of memory while creating the object to store all its attributes.
Usage of __slots__ reduce the wastage of space and speed up the program by allocating space for a fixed amount of attributes.
Example of python object with slots :
class GFG(object): Â Â Â Â Â Â __slots__=['a', 'b'] Â Â Â Â Â Â def __init__(self, *args, **kwargs): Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â self.a = 1Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â self.b = 2Â Â if __name__ == "__main__": Â Â Â Â Â instance = GFG() Â Â Â Â Â print(instance.__slots__) |
Output :
['a', 'b']
Example of python if we use dict :
class GFG(object): Â Â Â Â Â Â __slots__=['a', 'b'] Â Â Â Â Â Â def __init__(self, *args, **kwargs): Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â self.a = 1Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â self.b = 2Â Â if __name__ == "__main__": Â Â Â Â Â instance = GFG() Â Â Â Â Â print(instance.__dict__) |
Output :
AttributeError: 'GFG' object has no attribute '__dict__'
This error will be caused.
Result of using __slots__:
- Fast access to attributes
- Saves memory space
