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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>
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RULEBOOK: Python Performance
Load into coding subagents. ~200 tokens. See /papers/*.md for deep dives.
The Numbers
- Async overhead: 1,400x (sync 20ns, async 28us)
- Set vs list membership: 200x at 1000 items
- orjson vs stdlib json: 8x faster
- Exception raise: 6.5x (try/except is free)
- SQLite commit: 98% of write latency
Rules
Collections: Default to set for membership. Use dict.get() not check-then-access.
Async: Default sync. Only async for I/O >1ms. Use gather() not loops.
JSON: Default orjson. stdlib only for zero-deps requirement.
Exceptions: try/except is free. Use .get() for dicts. EAFP for <15% failure rate.
Strings: f-strings default. % for logging (deferred eval). join() for many parts.
Memory: @dataclass(slots=True) for 100+ instances.
Imports: TYPE_CHECKING for heavy types. Lazy imports in CLI tools.
Database: DiskCache for key-value. Batch SQLite writes. Reads cheap, writes expensive.
Source: python-numbers-everyone-should-know (Python 3.14.2, Apple Silicon)