<h2><strong>🚀 What I built</strong></h2>
<p>A production-ready cache stampede prevention library in Go with 3 strategies,</p>
<p>full Prometheus metrics, and a live Grafana dashboard.</p>
<hr>
<h2>🔥 The problem</h2>
<p>When a hot cache key expires, thousands of concurrent requests all miss at the same</p>
<p>time and hammer the database. This is a cache stampede — and it can take down your DB.</p>
<hr>
<h2>🛡️ 3 strategies implemented</h2>
<h3>1. Hard Lock + Stale-While-Revalidate</h3>
<ul>
<li>Only one process fetches the new value (Redis SETNX + Lua atomic script)</li>
<li>Everyone else gets served the stale value immediately — zero wait time</li>
</ul>
<h3>2, XFetch (probabilistic early refresh)</h3>
<ul>
<li>Formula: P = exp(-β × timeRemaining / δ)</li>
<li>Starts refreshing in the background before the key expires</li>
<li>No thundering herd because refreshes are spread out probabilistically</li>
</ul>
<h3>3. Request Coalescing (singleflight)</h3>
<ul>
<li>In-process deduplication: 50 concurrent goroutines → 1 real DB call</li>
<li>~13:1 dedup ratio visible in Grafana</li>
</ul>
<hr>
<h2>📊 Monitoring stack</h2>
<ul>
<li>Prometheus metrics for every decision point (hit/miss/stale/lock_contention)</li>
<li>Grafana dashboard with 9 panels auto-provisioned via docker-compose</li>
<li>Circuit Breaker state transitions (Closed → Open → Half-Open) tracked as gauges</li>
<li>Load test drives 1 000 req/s of sustained traffic</li>
</ul>
<hr>
<h3>🧱 Tech stack</h3>
<p>Go 1.2x· Redis 7 · Prometheus · Grafana · Docker Compose
Please give me a star for my repository if it is useful for you. Thanks</p>
<hr>
<p>🔗 GitHub: [<a href="https://github.com/0x48core/go-latency/tree/main/cache-stampede" target="_blank" rel="noopener nofollow">https://github.com/0x48core/go-latency/tree/main/cache-stampede</a>]</p>
<p><img src="https://media.daily.dev/image/upload/s--zFeYhEp7--/f_auto/v1778263070/ugc/content_a61035ca-45e3-47d9-bd8a-fa195914940b?_a=BAMAMiWQ0" alt="image.png"></p>


Augustus Nguyen

A Go library implementing three cache stampede prevention strategies: Hard Lock with Stale-While-Revalidate using Redis SETNX and Lua scripts, XFetch probabilistic early refresh, and Request Coalescing via singleflight. The project includes a full monitoring stack with Prometheus metrics, a Grafana dashboard with 9 auto-provisioned panels, circuit breaker tracking, and a load test at 1,000 req/s. The singleflight approach achieves roughly 13:1 deduplication ratio, reducing database pressure significantly.

Cache Stampede Prevention in Go — 1k req/s load test + full monitoring stack