__slots__ & Memory Optimisation

Also Known As

TL;DR

Explanation

By default, each Python object has a __dict__ (a dictionary) storing its attributes — this adds ~200 bytes overhead per instance and allows arbitrary attribute addition. Declaring __slots__ = ('x', 'y') creates fixed-slot descriptors instead, eliminating __dict__. Benefits: 40-60% less memory, faster attribute access (no dict lookup), prevents accidental typo attributes. Trade-offs: no dynamic attributes, no __dict__, no weakref by default (add '__weakref__' to slots). Python 3.10+ dataclasses support slots=True directly.

Common Misconception

Why It Matters

Common Mistakes

• Forgetting to add __slots__ to subclasses — a subclass without __slots__ still creates __dict__.
• Not including '__weakref__' in slots when the class needs to be weakly referenced.
• Using __slots__ on classes that need dynamic attribute assignment — slots forbid this.
• Forgetting that __slots__ makes pickle more complex — must implement __getstate__/__setstate__.

Code Examples

# No slots — each instance has a __dict__:
class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

import sys
p = Point(1, 2)
print(sys.getsizeof(p))  # ~48 bytes, plus ~200 bytes for __dict__
# 1 million Points: ~248MB

# With __slots__ — fixed layout, no __dict__:
class Point:
    __slots__ = ('x', 'y')
    def __init__(self, x, y):
        self.x = x
        self.y = y

import sys
p = Point(1, 2)
print(sys.getsizeof(p))  # ~56 bytes, no __dict__
# 1 million Points: ~56MB — 4x less memory

# Python 3.10+ dataclass with slots:
from dataclasses import dataclass
@dataclass(slots=True)
class Point:
    x: float
    y: float

References

• ↗ https://docs.python.org/3/reference/datamodel.html#slots

Tags

Related Terms