Architectural Resilience: Mitigating V8 Memory Leaks and Redis Cache Stampedes

<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>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 —

<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><em>Disclaimer: As a non-native English speaker, I used AI to help structure and polish this article.</em></p> <p>A few dayss ago, I shared <a

<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>Next.js ISR works great on a single pod. But the moment you scale to multiple replicas — whether on <strong>Kubernetes</strong>, <strong>ECS</strong>, <strong>Cloud

<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> Filling a maintainer's "Help needed": shipping a Next.js 16 Redis cache handler </h1> <p>Next.js 16 split caching into two distinct handler interfaces:</p> <ul> <li>