caching-patterns

Multi-layer caching optimizes web performance by storing data at browser, CDN, application, database, and framework levels. A strategic decision framework helps choose the right caching approach based on data volatility and access patterns to reduce server load.

Apply browser caching with `immutable, max-age=1y` for hashed static assets like JavaScript and CSS files. This configures HTTP cache headers to enforce long TTLs, reducing server load and bandwidth while ensuring clients reuse unchanged resources.

Stale-while-revalidate improves perceived load times for API responses by serving cached data immediately while fetching fresh data in the background. This caching technique ensures data freshness without making users wait for network requests.

Redis and in-memory caching patterns are supported for application-level caching. This approach allows you to store frequently accessed data closer to the application logic, significantly reducing database query times and improving response speeds.

Cache invalidation techniques and TTL management are guided by data volatility. By configuring appropriate expiration times and invalidation strategies, you balance optimizing performance with ensuring data freshness across CDN and application layers.

Framework-specific caching like Next.js is explicitly supported. You can integrate these caching strategies directly within your framework's architecture, combining TTL management and invalidation techniques with framework-level data fetching and rendering.