Caching is the Root of All Evil (Part 1): Why Redis is Corrupting Your Data

Caching is the Root of All Evil (Part 1): Why Redis is Corrupting Your Data

"My API is slow. I'll just stick Redis in front of it." We have all said this. You add the cache, latency drops from 200ms to 5ms, and you feel like a genius.
But you just planted a time bomb.

In this System Design deep dive, we explore why the standard "Cache-Aside" pattern (Read DB → Write Cache) is fundamentally broken for high-concurrency systems. We break down the "Stale Set" Race Condition—a hidden bug where a slow reader can accidentally overwrite your cache with old data after a writer has updated the database.

In this video, we cover:
• The Cache-Aside Trap: Why the standard code snippet (if val == null { val = db.Query(); cache.Set(val); }) is dangerous.
• The "Stale Set" Scenario:
◦ Reader A reads $100 from the DB.
◦ Reader A pauses (Network lag/GC).
◦ Writer B updates the DB to $120 and deletes the cache.
◦ Reader A wakes up and writes $100 to the cache.
◦ Result: The cache is now serving $100 while the DB says $120.
• The TTL Fallacy: Why setting a 5-minute TTL isn't a safety net—it just means you are guaranteed to serve wrong data for 5 minutes.
• The Thundering Herd: What happens when that TTL finally expires and 1,000 users hit your database at once.

Key Takeaway: Caching is not just a performance optimization; it is a State Management System. If you manage state poorly, you don't get speed—you get corruption.

🔔 Subscribe for Part 2, where we fix this using Leases and Versioning.

#SystemDesign #Redis #Caching #DistributedSystems #SoftwareArchitecture #RaceCondition #BackendEngineering

Видео Caching is the Root of All Evil (Part 1): Why Redis is Corrupting Your Data канала The Architect’s Notebook