The One Cache That Broke Our Treasure Hunt Engine
The article discusses the challenges faced in optimizing a treasure hunt game's caching system. Initially, a single Redis cache was used for both location and leaderboard data, leading to performance issues. The solution involved splitting the caches and implementing a more efficient architecture, resulting in improved latency and cache hit rates.
- ▪The initial caching strategy led to a significant drop in cache hit rates and increased latency.
- ▪The architecture was improved by separating the caches for location and leaderboard data, which reduced the load on the system.
- ▪After implementing the changes, the cache hit rate recovered to 94% and API latency improved significantly.
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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 27 The One Cache That Broke Our Treasure Hunt Engine #webdev #programming #architecture #systems The Problem We Were Actually Solving Our core game loop was: player scans QR → API writes record → Geo-indexer updates spatial index → leaderboard recalculates. We knew writes would be the hot path, so we cached scan → player_id → last_location in Redis with a 30 s TTL. Simple, fast, and we could afford to lose a few updates if the cache evaporated.
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