Caching Like Neo: Building a Distributed Cache

<p>Every high-traffic system eventually hits the same wall: your data store can't keep up. For us, the breaking point came when a simple product lookup — backed by Elasticsearch —

<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

<h1> Key-Value Stores: Design and Architecture </h1> <p>A key-value store is a non-relational database that maps unique keys to values. Keys must be unique, and each key maps

<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>

<p>If you have ever spun up a Redis Cluster, you probably ran into a strange, highly specific hardcoded number: <strong>16,384</strong>. </p> <p>No matter if you configure a tiny

<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