Network Background

Mastering Caching

Learn how and when to implement caching strategies to scale your systems.

Live Request Simulator

Visualize the difference between direct database access and cached responses.

laptop_mac
Client
memory
HIT
Cache (Redis)
Latency: ~5ms
database
Database
Latency: ~200ms
[7:10:18] Architecture switched to: NO-CACHE
// System Ready. Select architecture and send request.

How: Caching Patterns

There isn't one way to cache. The pattern you choose dictates data consistency and system complexity.

Cache-Aside (Lazy Loading)

The most popular pattern. The application is responsible for reading and writing to the cache.

thumb_up Pros

  • check_circleResilient to cache failure (app just goes to DB)
  • check_circleData model can differ from DB

thumb_down Cons

  • cancelFirst request is slow (cold start)
  • cancelData can become stale

Workflow

  1. 1. App asks Cache for data.
  2. 2. If HIT, App returns data.
  3. 3. If MISS, App queries Database.
  4. 4. App writes data to Cache for next time.

When Should You Cache?

Caching introduces complexity. It's not a silver bullet. Test your intuition with these real-world scenarios.

newspaper

Daily News Article

The text of a news article published this morning. It is read by millions but edited rarely.

currency_exchange

Real-time Stock Price

A stock ticker that changes every millisecond and requires strict accuracy for traders.

person_search

Complex Search Query

A search for "red shoes size 10" involving 4 table joins that takes 2 seconds to run.

Eviction Policies: LRU

Memory is finite. What happens when the cache is full?
LRU (Least Recently Used) is the industry standard.

Cache Memory (Max 4 Items)

Cache Empty

How LRU Works

1. When you access an item, it moves to the top (Most Recently Used).
2. If you add a new item and the cache is full (4/4), the item at the bottom (Least Recently Used) is evicted (deleted) to make room.

> LRU Visualizer initialized...