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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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/dev-architecture-blog/the-one-cache-that-broke-our-treasure-hunt-engine-425m |
| Publication time | Wed, 27 May 2026 10:05:42 +0000 |
| Retrieval time | 2026-05-27T10:07:58.288Z |
| Last seen | 2026-05-27T10:07:58.288Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 1qgc9Gy2Q_0i · 3 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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