7efd1368d1
Domain papers distilled from python-numbers-everyone-should-know: - async-overhead: 1,400x sync vs async overhead - collection-membership: 200x set vs list at 1000 items - json-serialization: 8x orjson vs stdlib - exception-flow: 6.5x exception overhead (try/except free) - string-formatting: f-strings > % > .format() - memory-slots: 69% memory reduction with __slots__ - import-optimization: 100ms+ for heavy packages - database-patterns: 98% commit overhead in SQLite RULEBOOK.md: ~200 token distillation for coding subagents 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
103 lines
2.5 KiB
Markdown
103 lines
2.5 KiB
Markdown
# Memory Optimization with __slots__ in Python
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**Domain Paper: Python Performance ADRs**
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**Date:** 2026-01-03
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**Source:** python-numbers-everyone-should-know benchmarks
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---
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## Executive Summary
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Python's `__slots__` mechanism provides **52-70% memory reduction** when creating many instances of the same class.
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**Key Finding**: For a class with 5 attributes, `__slots__` reduces instance memory from 694 bytes to 212 bytes (69% reduction).
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---
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## Benchmark Results: Memory Footprint
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### Single Instance Memory (5 Attributes)
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| Type | Memory (bytes) | vs Regular Class |
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|------|----------------|------------------|
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| Regular class | 694 | baseline |
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| `__slots__` class | 212 | -69% |
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| dataclass | 694 | same as regular |
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| `@dataclass(slots=True)` | 212 | -69% |
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| namedtuple | 228 | -67% |
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### At Scale (1,000 Instances)
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| Type | Total Memory |
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|------|--------------|
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| List of 1,000 regular class | 165.2 KB |
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| List of 1,000 `__slots__` class | 79.1 KB |
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**Memory Savings**: 52% reduction at scale
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---
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## Attribute Access Speed (Virtually Identical)
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| Operation | Regular | `__slots__` |
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|-----------|---------|-------------|
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| Read attr | 14.1 ns | 14.1 ns |
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| Write attr | 15.7 ns | 16.4 ns |
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---
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## Trade-offs
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### What __slots__ Prevents
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1. **No dynamic attribute assignment**
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2. **No `__dict__` access** (`vars()` doesn't work)
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3. **Inheritance complications**
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4. **No weak references by default**
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---
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## Practical Rules for Coding Agents
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### Rule 1: Instance Count Threshold
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```
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IF creating > 100 instances of the same class
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AND attributes are fixed at design time
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THEN consider __slots__ or @dataclass(slots=True)
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```
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### Rule 2: Prefer Slots Dataclass (Python 3.10+)
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```python
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@dataclass(slots=True)
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class User:
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id: int
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name: str
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email: str
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```
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### Rule 3: Don't Optimize Prematurely
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For < 100 instances, use regular classes for flexibility.
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### Rule 4: Document the Trade-off
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```python
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# Using __slots__ for memory efficiency (1000+ instances expected)
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__slots__ = ['x', 'y', 'z']
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```
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---
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## Summary
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| Aspect | Regular Class | `__slots__` Class |
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|--------|---------------|-------------------|
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| Memory (5 attrs) | 694 bytes | 212 bytes |
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| Read speed | 14.1 ns | 14.1 ns |
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| Dynamic attributes | Yes | No |
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| Best for | Flexibility | Many instances |
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**Bottom Line**: Use `__slots__` (or `@dataclass(slots=True)`) when creating many instances of fixed-attribute classes. For small numbers, stick with regular classes.
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---
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*Benchmark source: python-numbers-everyone-should-know*
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