Why We Stopped Using Redis (And Built a Sub-Microsecond Cache in Rust Instead)

<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

<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 —

<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,

<h2> The Problem We Were Actually Solving </h2> <p>We ran Veltrix, a Hytale server network with 14 shards across three continents. Our player count spiked past 800 concurrent

<p>Had yet another conversation yesterday with a team burning themselves on a Redis cache misconfiguration. Hot keys, thundering herd, memory policy oversights — the usual scars.

<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>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>Our Redis cluster was maxing out at 180k ops/sec across 12 nodes. KeyDB handled 850k ops/sec on 3 nodes. Same commands, same clients, zero… </p> <h3> I Migrated Redis to KeyDB