Caching Strategies for Performance

One-Liner

Techniques for storing frequently accessed data in a faster-access tier to improve response times and reduce the load on primary data sources.

What It Is

The various methods employed to store copies of data at different levels of a system (e.g., CPU cache, database cache, CDN, browser cache) to serve future requests for that data more quickly.

Why It Exists

To reduce latency, improve throughput, and decrease the load on backend systems by avoiding repeated computations or expensive data fetches.

How It Works

When a request comes in, the system first checks the cache.
If the data is in the cache (cache hit), it’s returned immediately.
If not (cache miss), the data is fetched from the slower primary source, served to the client, and then stored in the cache for future requests.

Tradeoffs

Pros

Significantly improves read performance.
Reduces load on origin servers.
Can enhance scalability.

Cons

Adds complexity to the system (cache invalidation, consistency issues).
Can lead to stale data if not managed properly.

Failure Modes

Cache Invalidation Issues: Serving stale or incorrect data due to improper cache invalidation (e.g., data updated in DB but old version still in cache).
Cache Stampede (Thundering Herd): Many clients simultaneously request data that is not in the cache, causing a flood of requests to the backend system.
Silent Cache Corruption: Data in the cache becomes corrupted without being detected, leading to incorrect responses.

Interview Traps

Only thinking of caches as “something like Redis.”
Not discussing the “cache invalidation problem.”

Real-World Usage

Database query caches, object caches (e.g., Redis, Memcached), HTTP caches (CDNs, browser caches).

Anti-Patterns

Caching data that changes very frequently, leading to constant invalidation and low cache hit rates.
Not having a clear strategy for cache invalidation, resulting in stale data.

Cache Invalidation
Write-through Cache
CDN
Database Optimization