Scale Game
について
Scale Gameスキルは、開発者がシステムを極端なスケール(1000倍の大きさ/小ささ、瞬時/1年間)でテストし、通常運用では隠された本質的な真実を明らかにするのに役立ちます。この手法は、スケーラビリティやエッジケースに不確実性がある場合、または本番環境のボリュームに対するアーキテクチャを検証する際に使用されます。ボリューム、速度、ユーザー数などの次元にわたってテストを行うことで、アルゴリズムの限界、並行性の問題、エラー処理の妥当性を明らかにします。
クイックインストール
Claude Code
推奨/plugin add https://github.com/Elios-FPT/EliosCodePracticeServicegit clone https://github.com/Elios-FPT/EliosCodePracticeService.git ~/.claude/skills/Scale GameこのコマンドをClaude Codeにコピー&ペーストしてスキルをインストールします
ドキュメント
Scale Game
Overview
Test your approach at extreme scales to find what breaks and what surprisingly survives.
Core principle: Extremes expose fundamental truths hidden at normal scales.
Quick Reference
| Scale Dimension | Test At Extremes | What It Reveals |
|---|---|---|
| Volume | 1 item vs 1B items | Algorithmic complexity limits |
| Speed | Instant vs 1 year | Async requirements, caching needs |
| Users | 1 user vs 1B users | Concurrency issues, resource limits |
| Duration | Milliseconds vs years | Memory leaks, state growth |
| Failure rate | Never fails vs always fails | Error handling adequacy |
Process
- Pick dimension - What could vary extremely?
- Test minimum - What if this was 1000x smaller/faster/fewer?
- Test maximum - What if this was 1000x bigger/slower/more?
- Note what breaks - Where do limits appear?
- Note what survives - What's fundamentally sound?
Examples
Example 1: Error Handling
Normal scale: "Handle errors when they occur" works fine At 1B scale: Error volume overwhelms logging, crashes system Reveals: Need to make errors impossible (type systems) or expect them (chaos engineering)
Example 2: Synchronous APIs
Normal scale: Direct function calls work At global scale: Network latency makes synchronous calls unusable Reveals: Async/messaging becomes survival requirement, not optimization
Example 3: In-Memory State
Normal duration: Works for hours/days At years: Memory grows unbounded, eventual crash Reveals: Need persistence or periodic cleanup, can't rely on memory
Red Flags You Need This
- "It works in dev" (but will it work in production?)
- No idea where limits are
- "Should scale fine" (without testing)
- Surprised by production behavior
Remember
- Extremes reveal fundamentals
- What works at one scale fails at another
- Test both directions (bigger AND smaller)
- Use insights to validate architecture early
GitHub リポジトリ
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