This article explains eight common software architecture patterns—from single‑database apps to microservices, caching, sharding, elastic scaling and multi‑datacenter deployment—detailing their designs, typical use cases, advantages, drawbacks, and practical implementation steps.
This article explains how to efficiently verify whether a user nickname is already registered on high‑traffic websites by combining Bloom filters with Redis caching, reducing database load and improving concurrency handling.
This article outlines the progressive evolution of e‑commerce system architecture from a monolithic 1.0 design through layered scaling, read/write separation, CDN caching, sharding, and finally unit‑based modularization, highlighting the performance bottlenecks and solutions at each stage.
This article details the end‑to‑end evolution of ZuanZuan's push notification platform, covering terminology, architecture, large‑scale holiday pushes, real‑time data handling, performance optimizations, multi‑channel integration, AB testing, and monitoring to achieve high throughput and reliability.
This article details JD.com’s journey of evolving the Elasticsearch cluster for its order system—from a chaotic initial deployment to a real‑time dual‑cluster architecture—highlighting scaling strategies, data sync methods, and the performance pitfalls they overcame.
During the COVID‑19 school closures, the education platform rapidly expanded its backend and database architecture, achieving a 20‑fold traffic increase through vertical upgrades, read/write separation, high‑availability redesign, and meticulous performance tuning, all within a few days of planning and execution.
This article walks through the step‑by‑step evolution of a high‑traffic e‑commerce backend—from a single‑machine setup to distributed caching, load‑balancing, database sharding, microservices, and finally cloud‑native deployment—highlighting the key technologies and design principles at each stage.
The article walks through the evolution of a high‑traffic e‑commerce backend—from a single‑machine setup to distributed databases, load‑balancing, micro‑services, and finally cloud‑native deployment—highlighting the technical challenges and design principles at each stage.
This article uses Taobao as a case study to trace the evolution of its server‑side architecture from a single‑machine setup to a cloud‑native, micro‑service ecosystem capable of handling tens of millions of concurrent users, highlighting key concepts, technologies, and design principles at each stage.
This article details how JD.com’s order center migrated its Elasticsearch cluster through multiple stages—from an initial unoptimized deployment to a real‑time dual‑cluster backup solution—addressing scalability, reliability, shard tuning, version upgrades, and data synchronization strategies to support billions of documents and hundreds of millions of daily queries.
Over six years the author chronicles the evolution of a backend system—from its initial three‑day launch, through successive capacity expansions, distributed refactoring, and micro‑service architecture—highlighting the challenges of scaling to millions of users, handling concurrency, and the continuous pursuit of simplicity and resilience.
This article details Xiaomi's evolution from a simple two‑server e‑commerce site to a sophisticated, decoupled architecture with the Notify asynchronous messaging system, explaining design decisions, message splitting, agent proxying, performance upgrades, and lessons for building resilient backend services.
This article chronicles the evolution of KuJiaLe's ExaCloud rendering platform from its 2013 GPU‑based prototype to a multi‑IDC, 2000‑node distributed system, detailing architectural redesigns, load‑balancing strategies, hybrid CPU/GPU processing, and operational lessons learned to achieve high‑throughput cloud rendering.
This article traces the evolution of a web system from a single‑server LAMP setup through service separation, caching, clustering, read/write splitting, CDN acceleration, distributed storage, NoSQL, business decomposition, and finally to distributed services with message queues, service frameworks, and service‑bus governance.
This article explains how LinkedIn engineered its instant‑messaging service using Server‑Sent Events, Java, Akka, and Play, and details key kernel and system‑level optimizations—such as socket backlog, temporary port exhaustion, and file‑descriptor limits—that enabled each server to handle hundreds of thousands of persistent connections.
This article chronicles Hotjar's evolution from a simple two‑server setup to a robust, eight‑server architecture handling billions of daily requests, sharing eight practical lessons on scaling, CDN usage, language choice, data storage, monitoring, and cost‑effective optimizations for fast‑growing web services.
This article explains how WeChat redesigned its red‑packet system for the 2016 Chinese New Year, detailing north‑south data separation, multi‑layer caching, asynchronous settlement, high‑concurrency controls, sharding strategies and graceful degradation to handle billions of red‑packet requests.
With growing website traffic, single‑machine deployments struggle, so this article examines four session‑management strategies—session stickiness, replication, centralized storage, and cookie‑based approaches—detailing their mechanisms, advantages, and drawbacks to help you select the best solution for scalable web applications.
