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Combining Redis’s distributed, high‑capacity cache with Caffeine’s ultra‑fast local cache creates a two‑level strategy where most reads hit the in‑process cache, fall‑backs use Redis, and only rare misses query the database, dramatically cutting latency, boosting throughput, and easing DB load.
The article explains cache‑aside consistency challenges and compares four update strategies—DB‑then‑cache, cache‑then‑DB, DB‑then‑delete, and delete‑then‑DB—showing that deleting the cache after a successful DB write offers the smallest inconsistency window, while recommending TTLs, message‑queue invalidation, and multi‑key coordination for robust eventual consistency.
Redis is a high‑performance, in‑memory NoSQL key‑value store offering persistence, rich data types, advanced structures, and robust commands, supporting caching, session storage, pub/sub, and leaderboards, while evolving through replication, Sentinel, clustering, and multithreaded proxies, with Tencent Cloud providing scalable, highly available managed Redis services.
Shopee’s scalable fund‑routing decision engine combines a configurable Go‑based rule engine with Redis‑backed, Lua‑driven quota control to dynamically match loan orders to external capital providers, enforce multi‑dimensional volume limits, achieve tens of thousands TPS, and provide extensible risk‑management capabilities across markets.
The article explains Redis’s in‑memory architecture, detailing memory components, object encodings, buffer limits, fragmentation, and forked‑process overhead, and offers practical optimization tips—such as using compact encodings, controlling client buffers, disabling THP, and enabling active defragmentation—illustrated by real‑world case studies.
For deduplicating game recommendations in Redis, RoaringBitMap outperforms intset and Bloom filters by storing 300 auto‑incrementing game IDs in roughly 0.5 KB—over twice the compression of intset and far smaller than the 29 KB Bloom filter—thereby cutting memory use, latency, and hardware costs.
This guide explains caching fundamentals, purposes, and optimal use cases, details hit rates and eviction policies such as FIFO, LRU, LFU, TTL/TTI, compares client‑side (HTTP, browser) and server‑side caches (CDN, Redis, Memcached), explores multi‑level architectures, common pitfalls like avalanche, penetration and breakdown, and best‑practice strategies including pre‑warming, update patterns, and consistency management.