Redis Cache
Leveraging Redis for in-memory caching to optimize performance.
Overview
Performance bottlenecks became obvious early on.
- Even with a small dataset, MySQL queries through Django’s ORM were painfully slow. - I considered giving up on the project—until I found the right solution:
Direct SQL calls + Redis caching. - Later, when I integrated FAISS, Redis turned out to be the key to solving Django-FAISS incompatibility.
Key Goals:
Optimize performance** by minimizing database calls and leveraging caching.
Reduce reliance on Django ORM** in favor of direct SQL queries for efficiency.
Use Redis to handle FAISS integration** and bypass Django’s limitations.
Complexity: Medium
Components
Redis for Caching
Implemented Redis as an in-memory cache to improve query performance and reduce database load.
SOARL Summary
MySQL performance was unacceptable—even with small datasets.
Needed a scalable caching solution to prevent excessive database calls.
Had to choose between Redis and Memcached** for caching.
Needed a caching strategy** that worked both locally and in GCP.
{“Implemented Redis caching** for”=>[“Frequently accessed DB table objects.”, “Preloading FAISS index results** for rapid retrieval.”]}
Updated the code generator** to auto-implement SQL queries + Redis caching.
Massively improved performance—queries became **instantaneous.
Avoided bottlenecks** that would have crippled the system at scale.
Redis wasn’t originally in my plan—but scaling issues force you to **adapt fast.
Had I not built code generators, refactoring the whole system **would have been a nightmare.
Instead, I wrote a new code pattern—and in under two hours, the entire platform was running smoothly.
Situation:
Obstacle:
Action:
Result:
Learning:
Key Learnings
- Scaling issues reveal themselves earlier than expected—especially when working with AI. - Caching isn’t optional—it’s a necessity when performance matters. - Code generators saved me days of refactoring—proving that automation is **critical to fast iteration.
Demos
Final Thoughts
Redis turned out to be the solution to a problem I didn’t even know I had.
Without caching, the platform wouldn’t have scaled—but by integrating SQL optimizations + Redis, performance transformed overnight. 🚀