Python __slots__ for Memory-Efficient Classes

Python objects use __dict__ for attribute storage by default, consuming significant memory when creating millions of instances.

Use __slots__ to declare fixed attributes:

# Without __slots__: each instance has a __dict__ (~200+ bytes overhead)
class PointDict:
    def __init__(self, x, y):
        self.x = x
        self.y = y

# With __slots__: no __dict__, fixed memory layout (~56 bytes per instance)
class PointSlots:
    __slots__ = ('x', 'y')
    def __init__(self, x, y):
        self.x = x
        self.y = y

# With inheritance
class Point3D(PointSlots):
    __slots__ = ('z',)  # Only add NEW attributes
    def __init__(self, x, y, z):
        super().__init__(x, y)
        self.z = z

# Combine with dataclasses (Python 3.10+)
from dataclasses import dataclass

@dataclass(slots=True)
class Point:
    x: float
    y: float

Memory savings: 40-60% per instance for simple objects. For 1M instances, this can mean hundreds of MB saved.

Python's __dict__ is flexible but wasteful when object structure is known at class definition time. __slots__ uses a C-level descriptor protocol that's both faster and more memory-efficient.

Creating many instances of simple data-holding classes