scalable-thinking

AI-consumed reference. Optimized for Claude to read during execution. Human-readable explanation: see docs/architecture/HIERARCHICAL_PLANNING.md or docs/getting-started/ depending on topic.

Skill: Scalable Thinking

Principle: Think Scalable, Build Simple.

Aspect	Scalable Thinking (WHAT)	KISS (HOW)
Data Model	Normalized, indexed	Simple queries
API	Versioned, RESTful	Standard CRUD
Code Structure	Feature-based	Flat hierarchy
Config	Centralized	Single file

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Where to Apply

Data Models

Separate related data into proper tables (not JSON strings). Enables querying, indexing, partitioning.

APIs

RESTful resource paths with versioning: /api/v1/users/:userId/processes. Cacheable, evolvable.

Database

Normalized tables with foreign keys and indexes. Not JSON blobs that can't be filtered.

File Structure

Feature-based: src/features/{feature}/{hooks,components,api}/ + src/shared/.

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Scalable Patterns

• Pagination: Cursor-based ?limit=20&cursor=abc with { nextCursor, hasMore }
• Centralized config: Single config object with env-based values
• Structured errors: AppError with code + statusCode

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Anti-Patterns

Anti-Pattern	Instead
Build for hypothetical 1M users	Simple monolith first
Pre-optimize with Redis	In-memory cache, add Redis when measured
Abstract for 1 use case	Direct impl, abstract at 2-3 examples
Microservices from day 1	Monolith, split when team/features grow

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Checklist

Before designing: Can model support 10x? Can I query needed data? Natural partition keys? API versioned? Pagination for lists?

During: Standard patterns? Simplest solution? Junior dev understandable? No abstractions until 3+ examples?

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When to Scale Up

Add complexity when: DB queries > 2s, API p95 > 500ms, error rate > 1%, 10x growth in 3 months, team > 5. Until then: keep simple, measure everything.

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