Python | Use of __slots__

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 : Python3 1==

class GFG(object):
      def __init__(self, *args, **kwargs):
                self.a = 1
                self.b = 2

if __name__ == "__main__":
     instance = GFG()
     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 : Python3 1==

class GFG(object):
      __slots__=['a', 'b']
      def __init__(self, *args, **kwargs):
                self.a = 1
                self.b = 2

if __name__ == "__main__":
     instance = GFG()
     print(instance.__slots__)

Output :

['a', 'b']

Example of python if we use dict : Python3 1==

class GFG(object):
      __slots__=['a', 'b']
      def __init__(self, *args, **kwargs):
                self.a = 1
                self.b = 2

if __name__ == "__main__":
     instance = GFG()
     print(instance.__dict__)

Output :

AttributeError: 'GFG' object has no attribute '__dict__'

This error will be caused. Result of using __slots__:

1. Fast access to attributes
2. Saves memory space