How I Built a Two-Level Cache to Serve Millions of Lookups in Under a Millisecon

<blockquote> <p>Skip the theory rabbit holes. This is the caching knowledge that shows up in system design interviews, code reviews, and the 2 AM production incidents nobody

<p>Hey dev.to community! 👋</p> <p>I've been working on a side project for a while now and finally got it to a point where I feel comfortable sharing it publicly. It's called

<p>Every backend engineer has heard the advice: add a cache. It will make your system faster. And they are right, it will. But a cache that is not properly understood is one of

<blockquote> <p>Cross-posted from <a href="https://runsite.app/blog/cache-aside-write-through-write-behind" rel="noopener noreferrer">the Runsite blog</a>.</p> </blockquote>

<ul> <li> <strong>Book:</strong> <a href="https://www.amazon.com/dp/B0GYMFPTWV" rel="noopener noreferrer">System Design Pocket Guide: Fundamentals</a> </li> <li> <strong>Also by

<p>If you've ever opened DevTools and watched the same API call fire <strong>over and over again</strong> — for data that hasn't changed in hours — this article is for you.</p>

<h2> 1. Intro — The Problem We Actually Had </h2> <p>Building real-time systems is often presented as a distributed systems problem.</p> <p>Kafka, stream processors, event buses,

<p>If a page reads from your database, render it <strong>on demand</strong>, not during the build. Prerendering it means your build container needs a route to Postgres, and on