lucidchart-performance-tuning

# Lucidchart Performance Tuning ## Overview Lucidchart documents can contain thousands of shapes and connectors — a single enterprise diagram may hold 500+ elements across multiple pages, making bulk reads and exports the primary API bottleneck. This skill covers caching document metadata, batching shape operations, and managing Lucid's rate limits to keep integrations responsive. ## Instructions 1. Implement Redis caching (or in-memory Map for development) with document-appropriate TTLs 2. Use cursor-based pagination for all document list operations to avoid incomplete results 3. Wrap API calls with the rate limit handler, especially for bulk shape updates and exports 4. Configure connection pooling with extended timeouts for export endpoints ## Prerequisites - Lucid OAuth2 client credentials with `lucidchart.document` scope - Redis instance for document/shape metadata caching - Node.js 18+ with native fetch - Understanding of Lucid document structure (documents, pages, shapes, lines) ## Caching Strategy ```typescript import Redis from "ioredis"; const redis = new Redis(process.env.REDIS_URL); // Document metadata is stable — cache 15 minutes // Shape data changes during active editing — cache 1 minute const TTL = { docList: 900, docMeta: 600, shapes: 60, exports: 300 } as const; async function getCachedDocument(docId: string): Promise<LucidDocument> { const key = `lucid:doc:${docId}`; const cached = await redis.get(key); if (cached) return JSON.parse(cached...