Evolution of Server‑Side Architecture from Single Machine to Cloud‑Native Scale

The article begins by introducing fundamental concepts such as distributed systems, high availability, clustering, load balancing, and proxy mechanisms to ensure readers understand the basics of architecture design.

It then traces the architectural evolution using an e‑commerce example, describing each major transition:

1. Single‑machine architecture: Initially, Tomcat and the database run on the same server, suitable for low traffic.

2. First evolution – separating Tomcat and database: Deploying them on separate machines improves performance but soon hits database bottlenecks.

3. Second evolution – adding local and distributed caches: Introducing memcached locally and Redis as a distributed cache reduces database load, while addressing cache consistency, penetration, and avalanche issues.

4. Third evolution – reverse‑proxy load balancing: Using Nginx or HAProxy distributes requests across multiple Tomcat instances, increasing concurrency but shifting the bottleneck to the database.

5. Fourth evolution – database read/write separation: Employing middleware such as MyCAT creates separate read replicas, improving read scalability.

6. Fifth evolution – business‑level database sharding: Different business domains use separate databases, reducing contention and enabling horizontal scaling.

7. Sixth evolution – splitting large tables: Horizontal partitioning (hash or time‑based) further distributes load; MPP databases like TiDB, Greenplum, and PostgreSQL‑XC are introduced.

8. Seventh evolution – LVS/F5 load balancing: Layer‑4 balancers provide higher throughput than Nginx, with keepalived ensuring high availability.

9. Eighth evolution – DNS round‑robin across data centers: DNS‑based traffic distribution enables multi‑data‑center scaling.

10. Ninth evolution – NoSQL and search engines: Technologies such as HDFS, HBase, Redis, Elasticsearch, Kylin, and Druid address big‑data storage, key‑value access, full‑text search, and analytical workloads.

11. Tenth evolution – splitting monolithic applications: Dividing code by business modules improves independent deployment and scaling.

12. Eleventh evolution – extracting common functions as microservices: Services like user management and payment are isolated, using frameworks such as Dubbo or Spring Cloud for governance.

13. Twelfth evolution – enterprise service bus (ESB): ESB unifies protocol conversion and reduces coupling, resembling SOA architecture.

14. Thirteenth evolution – containerization: Docker packages services, while Kubernetes orchestrates dynamic deployment, simplifying scaling and isolation.

15. Fourteenth evolution – cloud platforms: Moving to public IaaS/PaaS/SaaS provides elastic resources, reducing operational costs and enabling on‑demand scaling.

The article concludes with a set of architectural design principles, including N+1 redundancy, rollback capability, feature toggles, monitoring, multi‑active data centers, mature technology adoption, resource isolation, horizontal scalability, purchasing non‑core components, commercial hardware usage, rapid iteration, and stateless service design.