The article explains that layered architecture is not just a formalism for code organization but a fundamental safeguard that isolates responsibilities, changes, and replacements, preventing large systems from collapsing and enabling maintainable, scalable development.
This article examines the common benefits, drawbacks, and design principles of layered architecture across micro‑service, data‑warehouse, and protocol designs, illustrating each point with real‑world examples and offering practical guidance on when and how to apply layering effectively.
Layered architecture offers abstract stability, functional reuse, cohesion, hidden complexity, and scalability, but can introduce extra complexity, performance overhead, and dependency risk, so designers should retain essential layers, enforce one‑way cross‑layer calls, depend only on lower layers, keep lower layers stable, and ensure each layer has a clear purpose.
This article explains the concept, challenges, step‑by‑step process, organizational and tool requirements, capacity governance, planning, and AI‑driven prediction for full‑link load testing, illustrating how enterprises can ensure system capacity and stability during large‑scale online events.
The article examines the Chengdu COVID‑19 testing system failure, analyzes its root causes such as oversized MySQL tables and insufficient load handling, and then presents a step‑by‑step high‑concurrency roadmap—including single‑machine, service‑database separation, caching, load balancing, read/write splitting, sharding, hardware and DNS load balancing—to help backend developers design scalable systems.
This article reviews key concepts from the book "Software Architecture with Patterns" and discusses how architectural patterns such as layered architecture, microkernel, and GoF design patterns can guide the decomposition of monolithic applications and improve the extensibility of complex systems.
Performance optimization is a continuous, data‑driven practice that monitors response time and concurrency, applies techniques such as indexing, caching, parallelism, and asynchronous processing, and in Youzan’s live‑streaming product‑detail case reduced bottlenecks by adding multi‑level caches, circuit‑breaker fallbacks, and parallel sub‑task aggregation.
The article examines the fundamental drawbacks of traditional monolithic applications—such as excessive complexity, slow development speed, poor scalability, and rigid technology stacks—and explains why these issues motivate developers to adopt microservice architectures.
High concurrency refers to a short period where a system receives a massive number of requests, such as ticket‑booking spikes, and is measured by metrics like response time, throughput, QPS, and concurrent users, while multithreading is merely one programming technique to handle such load, not synonymous with high concurrency.
Medium’s engineering team recounts how they transitioned from a monolithic Node.js app to a microservice architecture, outlining three core design principles, practical strategies for service boundaries, data storage, networking, deployment, monitoring, and how to avoid common microservice pitfalls.
This article outlines a comprehensive web system architecture, detailing the layered components—from flexible load‑balancing strategies and service communication layers to various storage solutions—while highlighting practical considerations, trade‑offs, and best‑practice recommendations for building scalable, reliable backend services.
At the 10th Meitu Internet Technical Salon, experts presented classic video technology architecture and optimization case studies, detailing Meipai’s cost‑reduction and millisecond startup techniques, Inke’s live‑quiz system that scales to millions of concurrent users, and five strategies that cut Meipai live‑stream first‑screen time by more than 70%.
This article explains the reasons for breaking up tightly‑coupled monolithic systems, outlines preparation steps such as assessing business complexity and defining service boundaries, and provides detailed practical guidance on database sharding, migration, query refactoring, cut‑over plans, consistency, and post‑split stability.
