mindtickle-performance-tuning

# MindTickle Performance Tuning ## Overview MindTickle's API serves sales enablement data across courses, quizzes, and analytics — enterprise deployments tracking thousands of reps make bulk progress queries and report generation the primary bottlenecks. This skill covers caching learner data, batching progress operations, and handling rate limits during high-volume training campaigns. ## Instructions 1. Implement Redis caching (or in-memory Map for development) with TTLs matching data volatility 2. Use offset-based pagination for user and course lists to avoid incomplete result sets 3. Queue incoming completion webhooks through Redis to handle campaign burst traffic 4. Wrap all API calls with the rate limit handler before deploying to production ## Prerequisites - MindTickle API key with admin or integration scope - Redis instance for caching learner progress and report data - Node.js 18+ with native fetch - Webhook endpoint configured for course/quiz completion events ## Caching Strategy ```typescript import Redis from "ioredis"; const redis = new Redis(process.env.REDIS_URL); // Course catalog changes rarely — cache 30 minutes // User progress updates frequently during campaigns — cache 2 minutes const TTL = { courses: 1800, progress: 120, reports: 600, users: 900 } as const; async function getCachedCourses(orgId: string): Promise<MindTickleCourse[]> { const key = `mt:courses:${orgId}`; const cached = await redis.get(key); if (cached) return JSON.parse(cac...