The Perils of Premature Optimisation in Distributed Treasure Hunts
The article discusses the challenges faced by a company when scaling a new treasure hunt engine for concurrent users. Initially, a caching-first approach led to performance issues and system outages. After reevaluating their architecture, they implemented a more reliable and scalable system using a message queue and separate read-only database replicas, resulting in significant improvements in performance and uptime.
- ▪The company needed to scale a treasure hunt engine to tens of thousands of concurrent users within 12 weeks.
- ▪Their initial caching-first approach caused performance issues and a production outage lasting nearly 24 hours.
- ▪After redesigning their system with a message queue and separate read-only replicas, they improved median response times by a factor of 10.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3942461) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Lillian Dube Posted on May 22 The Perils of Premature Optimisation in Distributed Treasure Hunts #webdev #programming #architecture #systems Before I dive into the details, let me set the scene. Our company had just acquired a new feature: a treasure hunt engine that would allow users to create and share complex, real-time, multi-player hunts.
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