Caching

What is Caching?

Caching is a fundamental performance optimization technique that stores copies of frequently accessed data in fast-access storage layers. Instead of repeatedly querying a database or recomputing results, applications can serve data from cache, dramatically reducing latency and system load.

Why Caching Matters

In modern distributed systems, caching is essential for:

• Reducing Latency: Serve data from memory (nanoseconds) instead of disk (milliseconds)
• Lowering Database Load: Prevent excessive queries that can overwhelm databases
• Improving Scalability: Handle more concurrent users without proportional infrastructure costs
• Enhancing Resilience: Serve stale data if primary data source is temporarily unavailable

Common Caching Strategies

1. Cache-Aside: Application checks cache first, then database on miss
2. Write-Through: Write to cache and database simultaneously
3. Write-Behind: Write to cache immediately, database asynchronously
4. Read-Through: Cache automatically loads from database on miss

When to Use Caching

Cache when:

• Data is read frequently but updated infrequently
• Computation is expensive
• Database queries are slow
• You need to reduce backend load

Avoid caching when:

• Data changes constantly
• Cache invalidation is complex
• Memory is limited
• Data must always be fresh

Implementation Considerations

Cache Eviction Policies:

• LRU (Least Recently Used)
• LFU (Least Frequently Used)
• TTL (Time-To-Live)

Cache Layers:

• Browser cache
• CDN cache
• Application cache (Redis, Memcached)
• Database query cache

Real-World Examples

• Amazon: Product catalog caching reduces database load by 90%
• Netflix: Content metadata cached at CDN edge for instant loading
• Uber: Route calculations cached to handle millions of concurrent requests

Caching is not about complexity—it's about thoughtful placement of data closer to where it's needed.