Caching

When to Use

• Adding memoization to expensive pure functions or computations
• Setting HTTP cache headers on API responses or static assets
• Implementing a Redis or in-memory cache layer for database queries
• Configuring CDN caching rules for edge distribution
• Designing cache invalidation strategies for data that changes
• Optimizing Next.js data fetching with built-in caching primitives

When NOT to Use

• Data that must always be real-time and consistent (financial transactions, inventory counts during checkout) — caching introduces staleness
• Write-heavy workloads where invalidation cost exceeds the read savings
• Small datasets that are fast to compute or fetch — the overhead of cache management is not worth it