Caching Strategies

Caching is the practice of storing copies of data in a temporary, faster-access storage layer so that future requests for that data can be served more quickly. In system design, caching is one of the most impactful techniques for reducing latency, lowering database load, and improving overall throughput. Nearly every large-scale system relies on caching at multiple layers of the stack.

Understanding caching strategies means knowing not just where to cache, but how to keep cached data consistent, when to invalidate or refresh entries, and how to handle failures gracefully. A poorly designed cache can introduce stale data, thundering herd problems, or memory pressure -- so the tradeoffs matter as much as the performance gains.

What You'll Learn

• Caching Fundamentals - Core concepts including cache hits and misses, TTL, eviction policies (LRU, LFU, FIFO), and the tradeoffs between memory usage and hit rates.
• Caching Patterns - Common strategies such as cache-aside (lazy loading), write-through, write-behind, and read-through, and when to apply each one.
• Distributed Caching - Scaling caches across multiple nodes using tools like Redis and Memcached, including consistent hashing, replication, and partition tolerance.
• CDN & Edge Caching - Pushing cached content closer to users through content delivery networks and edge servers to minimize round-trip latency for static and dynamic assets.

Prerequisites

A solid understanding of client-server architecture, HTTP basics, and database fundamentals will help you get the most out of this topic. Familiarity with latency and throughput concepts from earlier system design topics is also useful.