Python slots=True: 8x Memory Cut in 10M Dataclass Instances

A 500MB Memory Leak That Wasn't a Leak

I was profiling a data pipeline that processed sensor readings — 10 million small dataclass instances per batch. Memory usage sat at 600MB. Then I added slots=True to the @dataclass decorator. Memory dropped to 75MB.

Same data. Same logic. One parameter change.

This isn't some niche optimization. If you're using Python dataclasses for anything beyond toy examples — API response models, batch processing, in-memory datasets — you're probably burning 8x more RAM than necessary. The fix is a single argument, but the details matter. Let me show you what actually happens under the hood, where it breaks, and when you shouldn't use it.

Photo by Christina Morillo on Pexels

Why Python Objects Are Secretly Expensive

Every Python object carries a hidden dictionary called __dict__. It stores instance attributes as key-value pairs. Flexible? Absolutely. Memory-efficient? Not even close.

Consider this dataclass:

python
from dataclasses import dataclass
from sys import getsizeof

@dataclass
class SensorReading:
    timestamp: float

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