How One Engineer Cut Database Queries by 95 Percent with a Cache

What this episode covers

In this episode, Lucas and Luna explore a specific case where a single engineer reduced database query volume by 95 percent by implementing a carefully designed caching layer. They walk through the before-and-after: a SaaS platform serving 50,000 daily users was hitting its Postgres database over 1,000 times per second during peak hours, causing latency spikes and near-daily connection pool exhaustion. The engineer, rather than scaling vertically or adding replicas, introduced a write-through cache using Redis, but with a twist — they cached only the read-heavy, rarely-changed data like user preferences and product catalog metadata. The result was a drop from 100 million queries per day to 5 million, and response times for cached endpoints fell from 200 milliseconds to under 10. Listeners will learn about cache invalidation strategies, the trade-off between consistency and performance, and how to identify the right data to cache. No theory without practice — this is a concrete, replicable approach for any engineer dealing with database bottlenecks. #DatabaseOptimization #Caching #Redis #Postgres #SystemDesign #PerformanceEngineering #BackendEngineering #Scalability #WriteThroughCache #CacheInvalidation #QueryReduction #Latency #SoftwareEngineering #TechPodcast #FexingoBusiness #BusinessPodcast #Technology #EngineeringBestPractices Keep every episode free: buymeacoffee.com/fexingo

PodParley-generated summary based on available episode metadata and transcript content.