dict classes

A regular class whose attributes are stored in the __dict__ attribute of every single instance. This is quite wasteful especially for objects with very few data attributes and the space consumption can become significant when creating large numbers of instances.

This is the type of class you get by default both with and without attrs.

slotted classes

A class that has no __dict__ attribute and defines its attributes in a __slots__ attribute instead. In attrs, they are created by passing slots=True to @attr.s.

Their main advantage is that they use less memory on CPython [1].

However they also come with a bunch of possibly surprising gotchas:

  • Slotted classes don’t allow for any other attribute to be set except for those defined in one of the class’ hierarchies __slots__:

    >>> import attr
    >>> @attr.s(slots=True)
    ... class Coordinates(object):
    ...     x = attr.ib()
    ...     y = attr.ib()
    >>> c = Coordinates(x=1, y=2)
    >>> c.z = 3
    Traceback (most recent call last):
    AttributeError: 'Coordinates' object has no attribute 'z'
  • Slotted classes can inherit from other classes just like non-slotted classes, but some of the benefits of slotted classes are lost if you do that. If you must inherit from other classes, try to inherit only from other slot classes.

  • Using pickle with slotted classes requires pickle protocol 2 or greater. Python 2 uses protocol 0 by default so the protocol needs to be specified. Python 3 uses protocol 3 by default. You can support protocol 0 and 1 by implementing __getstate__ and __setstate__ methods yourself. Those methods are created for frozen slotted classes because they won’t pickle otherwise. Think twice before using pickle though.

  • Slotted classes are weak-referenceable by default. This can be disabled in CPython by passing weakref_slot=False to @attr.s [2].

  • Since it’s currently impossible to make a class slotted after it’s created, attrs has to replace your class with a new one. While it tries to do that as graciously as possible, certain metaclass features like __init_subclass__ do not work with slotted classes.

[1]On PyPy, there is no memory advantage in using slotted classes.
[2]On PyPy, slotted classes are naturally weak-referenceable so weakref_slot=False has no effect.