Three‑High System Construction: Performance, Concurrency, and Availability – A Backend Engineering Methodology

The author introduces the concept of a "three‑high" system—high performance, high concurrency, and high availability—as a historical battle against software complexity, distinguishing technical complexity (performance, concurrency, availability) from business complexity (modeling and abstraction).

Performance Chapter : Emphasizes that high performance is the foundation for the other two goals. It outlines a methodology that first identifies performance‑affecting factors—computation, communication, and storage—and then optimizes read/write paths. Key practices include leveraging local and distributed caches, distinguishing read‑heavy and write‑heavy workloads, and applying appropriate cache‑database coupling strategies (e.g., cache‑aside for read‑dominant services, write‑through with async DB updates for write‑dominant services). The article also discusses asynchronous processing for spike‑traffic scenarios such as flash‑sale order handling.

Concurrency Chapter : Describes how to increase throughput by scaling horizontally (adding machines, sharding, expanding groups) and vertically (adopting micro‑services via DDD, migrating from monolith to SOA to service mesh). It explains horizontal scaling through load‑balanced clusters and vertical scaling through database sharding, master‑slave replication, and data partitioning. Hot‑key problems are mitigated by local caching or key‑randomization techniques.

Availability Chapter : Details fault‑tolerance mechanisms across application, storage, and deployment layers. Application‑level techniques include rate limiting (token bucket, leaky bucket, sliding window), circuit breaking, timeout settings following the funnel principle, retry policies with idempotency considerations, and isolation (system‑level, environment‑level, data‑level, core/non‑core flow, read/write, thread‑pool). Storage‑level strategies cover replication (master‑slave, multi‑master, leaderless) and sharding (range‑based and hash‑based) for MySQL, Redis, Elasticsearch, and Kafka. Deployment‑level practices involve multi‑datacenter, multi‑region redundancy, containerized Docker deployments, and load‑balanced failover.

The article also shares practical DDD implementation for a retail logistics platform, illustrating domain segmentation (product, order, payment, fulfillment) and the importance of forward and backward compatibility during system upgrades. Throughout, diagrams (omitted here) illustrate architectures such as cache‑DB coupling, horizontal scaling topology, DDD domain models, and replication/sharding layouts.

Finally, the author summarizes the three‑high methodology, acknowledges the ongoing recruitment for JD Logistics Platform Technology, and provides references to related technical articles.