System Design Latency and Throughput🌱 Beginner

Caching is the ultimate weapon in System Design. It involves storing the result of an expensive, slow computation in ultra-fast memory (RAM) so subsequent users get the answer instantly.

What is Caching?

Imagine a restaurant. Cooking a complex soup takes 4 hours (a slow Database Query). If every customer had to wait 4 hours, the restaurant would fail. Instead, the chef cooks a massive batch in the morning and keeps it in a warm pot on the counter (the Cache). When a customer orders, it is served in 5 seconds.

Why is it the Best Optimization?

A standard SQL database reading from a hard drive might take 50 milliseconds to return complex joined data. A cache like Redis or Memcached reading from RAM returns it in 0.5 milliseconds. Adding a cache can speed up your application by a factor of 100x while simultaneously saving your database from crashing under load.

How to Implement Caching

The most common architecture is the Cache-Aside pattern:

1. User requests a viral news article.
2. The Server checks Redis (the Cache).
3. If found (Cache Hit!): Return the article instantly.
4. If NOT found (Cache Miss):
   - The Server queries the slow PostgreSQL Database.
   - The Server saves the result into Redis for the next user.
   - Return the article to the user.