__slots__ in Dataclasses for Memory Efficiency

Python objects store instance attributes in a __dict__ per instance, consuming significant memory when millions of instances are created. dataclasses do not enable slots by default.

Use slots=True in dataclasses (Python 3.10+) to eliminate per-instance __dict__.

from dataclasses import dataclass
import sys

@dataclass
class Point:
    x: float
    y: float

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

p1 = Point(1.0, 2.0)
p2 = PointSlotted(1.0, 2.0)

print(sys.getsizeof(p1))  # ~56 bytes plus __dict__ ~232 bytes
print(sys.getsizeof(p2))  # ~56 bytes no __dict__

# Slotted classes cannot have arbitrary attributes:
p2.z = 3.0  # AttributeError: 'PointSlotted' has no attribute 'z'

# For Python 3.9 and earlier:
@dataclass
class LegacySlotted:
    __slots__ = ('x', 'y')
    x: float
    y: float

Python instances store attributes in a dict, which has ~200 bytes of overhead. __slots__ replaces the dict with fixed-size array slots, reducing memory 50-80% for large numbers of instances.