Look-aside pattern vs Write-Through. LRU Eviction explained. Persistence trade-offs (RDB vs AOF). Single-threaded nature of Redis.
Why does my server crash? OS's desperate struggle to manage limited memory. War against Fragmentation.
A deep dive into Robert C. Martin's Clean Architecture. Learn how to decouple your business logic from frameworks, databases, and UI using Entities, Use Cases, and the Dependency Rule. Includes Screaming Architecture and Testing strategies.
Two ways to escape a maze. Spread out wide (BFS) or dig deep (DFS)? Who finds the shortest path?
Fast by name. Partitioning around a Pivot. Why is it the standard library choice despite O(N²) worst case?
Here's what I've come to understand after working through Redis in depth:
Redis is more than a cache. Data structure store, message broker, session manager, distributed lock... it's versatile.
No single correct caching strategy. Read-heavy? Cache-Aside. Consistency critical? Write-Through. Write performance matters? Write-Behind. Context-dependent.
Memory management is key. Proper LRU, LFU, TTL configuration ensures efficient memory usage.
RDB + AOF combination is best for persistence. RDB for fast backups, AOF for data safety.
Single-threading is a double-edged sword. Guarantees atomicity but O(N) commands freeze everything.
Always watch for Thundering Herd. Cache expiration can crash your DB. Use locking or early expiration.
Leverage data structures like Sorted Set and HyperLogLog. Solves problems like leaderboards and unique counting elegantly.
Need high availability? Use Sentinel or Cluster. But overkill for small projects.
Biggest takeaway from studying this: Redis saves systems when used correctly, complicates them when used wrong.
I hope this writeup helps someone working through the same questions.
