The article explores the history, architecture, and massive scalability of China’s 12306 railway ticketing system, highlighting its distributed design, real‑time seat allocation, and ability to handle billions of transactions during peak travel periods.
This guide explains how to optimize Nginx settings and adjust Linux kernel parameters—such as worker processes, connection limits, caching, HTTP/2, rate limiting, and TCP tweaks—to reliably handle massive traffic spikes while maintaining high availability and performance.
This article explains how to design a backend architecture that pre‑generates article HTML with FreeMarker, stores files on cloud servers, caches data in Redis and local memory, and uses LVS, Nginx, and Sentinel to safely handle sudden traffic spikes from viral news.
This article details how Bilibili’s membership‑purchase platform tackled massive traffic spikes by redesigning the order call chain, introducing concurrent and asynchronous processing, and applying a sharding strategy that split databases and tables, ultimately boosting latency performance and supporting over 4,000 TPS during peak sales.
The article outlines a comprehensive, multi‑layer architecture—including DNS routing, L4/L7 load balancing, micro‑service or monolithic deployment, caching, database sharding, hybrid‑cloud deployment, elastic scaling, and tiered degradation—to reliably support systems handling from millions to billions of daily active users under sudden traffic spikes.
This article details how JD.com designed, scaled, and refined its mask reservation backend during the COVID‑19 pandemic, addressing extreme traffic spikes, hot‑key issues, MySQL bottlenecks, and introducing a circuit‑breaker mechanism to protect the overall transaction system.
This article explains how large‑scale internet applications can use various caching patterns—such as Cache‑Aside, Read‑Through, Write‑Through, and Write‑Behind—to alleviate database pressure, maintain data consistency, and avoid pitfalls like cache penetration, avalanche, and thundering under extreme traffic loads.
This article explains how large‑scale internet applications can use various caching patterns—such as Cache‑Aside, Read‑Through, Write‑Through, and Write‑Behind—to reduce database load, avoid consistency problems, and mitigate high‑concurrency issues like cache avalanche and penetration.
This article explains why massive traffic spikes can cripple database performance, introduces cache layers and common caching patterns, and offers practical solutions to consistency, concurrency, penetration, and avalanche problems for large‑scale internet applications.
During Alibaba’s Double‑11 shopping festivals, Tmall’s product‑detail and shop pages faced traffic spikes dozens of times higher than normal, prompting a multi‑stage architectural evolution—from server‑side staticization and distributed caching to a unified CDN‑based system that delivers high availability, low latency, and efficient cache invalidation.
The article details JD.com's technical strategies for scaling product detail pages under massive traffic, covering service decomposition, KV‑based dynamic rendering with Nginx+Lua, unified service gateways, isolation, caching, monitoring, degradation, and performance testing to achieve sub‑50 ms response times.
This article explains JD's technical practices for handling massive traffic on product detail pages, covering front‑end rendering, KV storage, Nginx + Lua dynamic templating, unified service systems, monitoring, caching strategies, service isolation, and automated degradation mechanisms.
This article examines the critical issues of massive data handling, concurrency, file storage, relational design, indexing, distributed processing, AJAX usage, security, clustering, and OpenAPI trends that developers must address when architecting large, high‑interaction web sites.
During Double 11, JD.com’s product detail page handled tens of billions of requests without downtime by introducing a unified service architecture that consolidates promotion, inventory, recommendation, and other functions behind a single Nginx+Lua entry point, employing multi‑level caching, service isolation, and flexible degradation mechanisms.
Suning’s technical team shares how they prepared for Double‑11 by splitting monolithic services into focused modules, building a robust foundational platform with cloud, middleware, and monitoring tools, refining R&D processes, and implementing comprehensive load‑testing, optimization, and emergency response plans to ensure system stability under massive traffic.
This article explains why large‑scale front‑end pages like Taobao’s homepage need reliable fallback mechanisms, outlines common failure scenarios, and presents practical strategies such as request retries, local caching, and backup APIs to keep the UI functional under high traffic and network issues.
This article examines the key challenges of building large‑scale, high‑interaction web 2.0 platforms—including massive data processing, concurrency control, file storage, indexing, distributed architecture, AJAX usage, security, synchronization, clustering, and OpenAPI integration—offering practical considerations for robust backend design.
The article explains why high‑traffic web pages need robust fallback and disaster‑recovery mechanisms, outlines common failure scenarios, and presents practical solutions such as request retries, local caching, backup interfaces, and hard‑coded data with illustrative JavaScript code.
This article compares static file serving and Redis‑based page caching for heavily visited sites, outlining their advantages and drawbacks such as speed, update frequency, database load, implementation complexity, and the need for dynamic processing.
The article explains why adding servers and shifting traffic, often dismissed as low‑effort tricks, actually require substantial architectural planning, state management, load balancing, sharding, and multi‑datacenter coordination to reliably handle high‑traffic scenarios.
Learn how to fine‑tune Nginx by adjusting key global, events, and HTTP settings—such as worker processes, connection limits, gzip compression, and file caching—to maximize throughput and reduce latency under heavy client loads, with a complete example configuration and restart instructions.
