Ask a dev to speed up a slow app and the first word out of their mouth is usually "Redis." That instinct isn't wrong, but it's incomplete. Redis solves one specific problem, shared fast key value lookups across multiple servers, and a huge number of performance issues never need to touch it at all.
Here's the framing that actually helps: there isn't one cache, there are four. A request from a browser passes through the browser's own cache, then a CDN, then your application's in memory or Redis layer, and finally the database's own cache, before any of your code runs.
In the full guide, I cover:
- Browser caching with
Cache-Control: max age, no cache vs no store, and immutable - CDN caching, and why authenticated or per user data shouldn't go anywhere near it
- In process vs distributed caching, and where each one breaks down at scale
- Cache aside, write through, and write behind patterns, with JS and Python examples
- TTL expiry, event driven invalidation, and versioned keys for handling staleness
- A decision table mapping common data types to the right cache layer
Plus a free Redis memory calculator to help size your instance before you provision anything.
Read the full breakdown here 👇
https://devencyclopedia.com/blog/caching-strategies-explained
