Web System Load Balancing, Caching, and Scaling Strategies for High Traffic

When a web system’s daily visits grow from 100,000 to 10 million or even over 100 million, performance pressure increases dramatically, requiring multi‑level caching and architectural upgrades at each traffic stage.

Web Load Balancing – Various strategies are introduced: HTTP redirection (302), reverse‑proxy load balancing (e.g., Nginx), IP‑level load balancing (LVS‑NAT, LVS‑RD, LVS‑TUN), DNS load balancing, and DNS/GSLB combined with CDN to route users to the nearest server.

Database Caching and Optimization – Internal MySQL caching (proper indexing, thread‑cache, innodb_buffer_pool), connection pooling (pconnect, swoole), sharding/partitioning, and multi‑node MySQL setups (master‑slave, master‑master, read/write splitting) are discussed, along with multi‑threaded binlog synchronization.

Cache Layers Between Web and DB – Page static‑generation, single‑node memory caches (Redis/Memcached), cache clusters (Redis Cluster), and write‑reduction techniques (batch updates, queueing) are presented to offload read/write pressure from the database.

NoSQL and Hybrid Storage – When relational databases become a bottleneck, key‑value stores such as Redis can serve as both cache and persistent storage for hot data.

Geographic Deployment – Core‑centralized, node‑distributed architecture reduces latency for distant users; node disaster‑recovery and overload protection (reject‑new‑connections, traffic diversion) ensure high availability.

Conclusion – Scaling a web system is an iterative process of identifying bottlenecks, applying appropriate load‑balancing, caching, and database strategies, and continuously optimizing as traffic grows.