System Design 101: Caching Strategies That Actually Work
Cache-aside, write-through, write-back, and eviction policies compared with real-world latency benchmarks.
Why Caching is Critical
In high-throughput systems, reading from disk or relational databases is often the primary bottleneck. A well-designed multi-tiered caching layer with Redis or Memcached reduces database load by up to 90%.
Cache-Aside vs Write-Through Patterns
Cache-Aside (Lazy Loading): The application first queries the cache. On a cache miss, it reads from the primary database, writes the entry into the cache, and returns it. Best for read-heavy workloads where data changes infrequently.
Write-Through: The application writes data to the cache, and the cache synchronously updates the database. Ensures data is always fresh in cache at the cost of higher write latency.
Rule of thumb: Always set Time-To-Live (TTL) on cache keys to prevent memory saturation and stale data accumulation.
Eviction Policies
- LRU (Least Recently Used): Evicts the item not accessed for the longest time
- LFU (Least Frequently Used): Evicts the item accessed the fewest times
- TTL-based expiration: Automatically expires stale keys after a set duration
Reinforce these concepts
Practice with flashcards, test code in Web IDE, or take 60s speed drills on CodeShot.