Redis Optimization: How Local Caching Unlocked 10x Scalability
While working on a backend system supporting millions of users , Redis was chosen as the go-to solution for real-time data—sessions, counters, recommendations, you name it. The Redis setup ran on Google Cloud Memorystore’s lowest configuration , with best-practice TTLs, eviction, and well-designed keys baked in. But as user traffic ramped up, a subtle bottleneck appeared. Surprisingly, it was not the memory or dataset size that held us back—our entire hot data set was under 5GB and always fresh. Instead, the challenge was the enormous number of direct requests: every microservice and API call was reaching out to Redis in real time, leading to network congestion, latency, and a stretched-thin Redis instance. Problem: More Calls, Not More Data We looked at options. Scaling hardware felt excessive since memory and CPU were already sufficient for the modest data set. It wasn’t the amount of information, but the pattern of access —thousands of tiny...