Cache Usage Techniques and Design Strategies

Cache can significantly speed up application read/write operations and lower backend load; the article first analyzes the benefits (accelerated reads, reduced backend pressure) and costs (data inconsistency, higher code maintenance, increased ops effort) of adding a cache layer.

Typical usage scenarios include heavy computational workloads and request‑response acceleration, with examples of direct SQL queries like select*from table where id= that can be served by Redis at tens of thousands of ops per second.

Cache update strategies are discussed: eviction algorithms (LRU/LFU/FIFO), timeout expiration (using Redis expire ), and active updates (e.g., message‑driven invalidation). A comparison chart illustrates trade‑offs, and recommendations suggest using max‑memory policies for low‑consistency workloads and combining expiration with active updates for high‑consistency needs.

Granularity control advises balancing data reuse, memory consumption, and code maintainability when choosing cache keys, typically recommending Redis for the cache layer and MySQL for storage.

Penetration problems are mitigated by caching empty objects (with short TTL) and employing Bloom filters as a first‑level check to avoid unnecessary storage hits; the Bloom filter principle and its application in large‑scale recommendation systems are explained.

Bottom‑hole (no‑bottom‑hole) issues in distributed caches are addressed by optimizing batch operations: serial commands, node‑aware serial I/O, parallel I/O (with multithreading achieving O（1） network cost), and hash‑tag grouping to minimize network round‑trips.

Avalanche prevention focuses on high cache availability (e.g., Redis Sentinel/Cluster), backend rate‑limiting and degradation, and pre‑deployment failure drills to ensure resilience when the cache layer fails.

Hot‑key rebuild strategies include mutex locks using Redis SETNX to ensure only one thread rebuilds a cache entry, and “never‑expire” designs that combine logical expiration with background refresh, acknowledging temporary inconsistency trade‑offs.